{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/spectral-clustering/papers/4","list_of":"/method/spectral-clustering","method":"Spectral Clustering","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":4,"pages_in_order":6,"rows_per_page":100,"rows":[301,400],"of":517,"counts":{"archive_papers_tagged":517,"with_a_code_link":136,"where_syntology_ran_a_sample":20,"not_listed_spam_title":0,"listed":517,"listed_where_code_ran":20,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":17,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":17,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/spectral-clustering","prev":"/method/spectral-clustering/papers/3","next":"/method/spectral-clustering/papers/5","papers":[{"paper":"/paper/large-scale-multi-view-subspace-clustering-in","slug":"large-scale-multi-view-subspace-clustering-in","title":"Large-scale Multi-view Subspace Clustering in Linear Time","date":"2019-11-21","arxiv_id":"1911.09290","n_code_links":2,"syntology":null},{"paper":null,"slug":"dc2-a-divide-and-conquer-algorithm-for-large","title":"$DC^2$: A Divide-and-conquer Algorithm for Large-scale Kernel Learning with Application to Clustering","date":"2019-11-16","arxiv_id":"1911.06944","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimality-of-spectral-clustering-for","title":"Optimality of Spectral Clustering in the Gaussian Mixture Model","date":"2019-11-01","arxiv_id":"1911.00538","n_code_links":0,"syntology":null},{"paper":null,"slug":"regularized-non-negative-spectral-embedding","title":"Regularized Non-negative Spectral Embedding for Clustering","date":"2019-11-01","arxiv_id":"1911.00179","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiple-sample-clustering","title":"Multiple Sample Clustering","date":"2019-10-22","arxiv_id":"1910.09731","n_code_links":0,"syntology":null},{"paper":null,"slug":"sparse-dense-subspace-clustering","title":"Sparse-Dense Subspace Clustering","date":"2019-10-20","arxiv_id":"1910.08909","n_code_links":0,"syntology":null},{"paper":null,"slug":"multiclass-spectral-feature-scaling-method","title":"Multiclass spectral feature scaling method for dimensionality reduction","date":"2019-10-16","arxiv_id":"1910.07174","n_code_links":0,"syntology":null},{"paper":"/paper/deep-clustering-by-gaussian-mixture","slug":"deep-clustering-by-gaussian-mixture","title":"Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph Embedding","date":"2019-10-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"subspace-structure-aware-spectral-clustering","title":"Subspace Structure-Aware Spectral Clustering for Robust Subspace Clustering","date":"2019-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"salient-instance-segmentation-via-subitizing","title":"Salient Instance Segmentation via Subitizing and Clustering","date":"2019-09-29","arxiv_id":"1909.13240","n_code_links":0,"syntology":null},{"paper":null,"slug":"clustering-uncertain-data-via-representative","title":"Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning","date":"2019-09-27","arxiv_id":"1909.12514","n_code_links":0,"syntology":null},{"paper":null,"slug":"conformal-prediction-based-spectral","title":"Conformal Prediction based Spectral Clustering","date":"2019-09-17","arxiv_id":"1909.07594","n_code_links":0,"syntology":null},{"paper":"/paper/multi-graph-fusion-for-multi-view-spectral","slug":"multi-graph-fusion-for-multi-view-spectral","title":"Multi-graph Fusion for Multi-view Spectral Clustering","date":"2019-09-16","arxiv_id":"1909.06940","n_code_links":1,"syntology":null},{"paper":null,"slug":"concentration-of-kernel-matrices-with","title":"Concentration of kernel matrices with application to kernel spectral clustering","date":"2019-09-07","arxiv_id":"1909.03347","n_code_links":0,"syntology":null},{"paper":null,"slug":"online-pedestrian-group-walking-event","title":"Online Pedestrian Group Walking Event Detection Using Spectral Analysis of Motion Similarity Graph","date":"2019-09-03","arxiv_id":"1909.01258","n_code_links":0,"syntology":null},{"paper":null,"slug":"similarity-kernel-and-clustering-via-random","title":"Similarity Kernel and Clustering via Random Projection Forests","date":"2019-08-28","arxiv_id":"1908.10506","n_code_links":0,"syntology":null},{"paper":null,"slug":"nuclear-instance-segmentation-using-a","title":"Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework","date":"2019-08-27","arxiv_id":"1908.10356","n_code_links":0,"syntology":null},{"paper":"/paper/consistent-community-detection-in-continuous","slug":"consistent-community-detection-in-continuous","title":"CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation","date":"2019-08-19","arxiv_id":"1908.06940","n_code_links":1,"syntology":null},{"paper":null,"slug":"3d-rigid-motion-segmentation-with-mixed-and","title":"3D Rigid Motion Segmentation with Mixed and Unknown Number of Models","date":"2019-08-16","arxiv_id":"1908.06087","n_code_links":0,"syntology":null},{"paper":null,"slug":"estimation-of-spectral-clustering-hyper","title":"Bi-cross validation for estimating spectral clustering hyper parameters","date":"2019-08-10","arxiv_id":"1908.03747","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-kernel-learning-for-clustering","title":"Deep Kernel Learning for Clustering","date":"2019-08-09","arxiv_id":"1908.03515","n_code_links":0,"syntology":null},{"paper":null,"slug":"hermitian-matrices-for-clustering-directed","title":"Hermitian matrices for clustering directed graphs: insights and applications","date":"2019-08-06","arxiv_id":"1908.02096","n_code_links":0,"syntology":null},{"paper":null,"slug":"large-scale-sparse-subspace-clustering-using","title":"Large-Scale Sparse Subspace Clustering Using Landmarks","date":"2019-08-02","arxiv_id":"1908.00683","n_code_links":0,"syntology":null},{"paper":"/paper/lstm-based-similarity-measurement-with","slug":"lstm-based-similarity-measurement-with","title":"LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization","date":"2019-07-23","arxiv_id":"1907.10393","n_code_links":1,"syntology":null},{"paper":"/paper/sequence-level-semantics-aggregation-for","slug":"sequence-level-semantics-aggregation-for","title":"Sequence Level Semantics Aggregation for Video Object Detection","date":"2019-07-15","arxiv_id":"1907.06390","n_code_links":2,"syntology":null},{"paper":null,"slug":"spacetime-graph-optimization-for-video-object","title":"Spacetime Graph Optimization for Video Object Segmentation","date":"2019-07-07","arxiv_id":"1907.03326","n_code_links":0,"syntology":null},{"paper":"/paper/a-spectral-approach-to-unsupervised-object","slug":"a-spectral-approach-to-unsupervised-object","title":"A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time","date":"2019-07-05","arxiv_id":"1907.02731","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["bit-ml/sfseg"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"k-is-the-magic-number-inferring-the-number-of","title":"k is the Magic Number -- Inferring the Number of Clusters Through Nonparametric Concentration Inequalities","date":"2019-07-04","arxiv_id":"1907.02343","n_code_links":0,"syntology":null},{"paper":"/paper/a-flexible-em-like-clustering-algorithm-for","slug":"a-flexible-em-like-clustering-algorithm-for","title":"A flexible EM-like clustering algorithm for noisy data","date":"2019-07-02","arxiv_id":"1907.01660","n_code_links":2,"syntology":{"ran":4,"of":5,"n_ran_checked":3,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 1 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["violetr/fem"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"the-spectacl-of-nonconvex-clustering-a","title":"The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering","date":"2019-07-01","arxiv_id":"1907.00680","n_code_links":0,"syntology":null},{"paper":"/paper/attributed-graph-clustering-a-deep","slug":"attributed-graph-clustering-a-deep","title":"Attributed Graph Clustering: A Deep Attentional Embedding Approach","date":"2019-06-15","arxiv_id":"1906.06532","n_code_links":3,"syntology":{"ran":7,"of":8,"n_ran_checked":6,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"mima-mapper-induced-manifold-alignment-for","title":"MIMA: MAPPER-Induced Manifold Alignment for Semi-Supervised Fusion of Optical Image and Polarimetric SAR Data","date":"2019-06-13","arxiv_id":"1906.05512","n_code_links":0,"syntology":null},{"paper":null,"slug":"dcef-deep-collaborative-encoder-framework-for","title":"Multi-local Collaborative AutoEncoder","date":"2019-06-12","arxiv_id":"1906.05173","n_code_links":0,"syntology":null},{"paper":null,"slug":"nonconvex-approach-for-sparse-and-low-rank","title":"Nonconvex Approach for Sparse and Low-Rank Constrained Models with Dual Momentum","date":"2019-06-06","arxiv_id":"1906.02433","n_code_links":0,"syntology":null},{"paper":null,"slug":"glocal-incorporating-global-information-in","title":"Glocal: Incorporating Global Information in Local Convolution for Keyphrase Extraction","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"190600098","title":"Spectral Perturbation Meets Incomplete Multi-view Data","date":"2019-05-31","arxiv_id":"1906.00098","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatially-constrained-spectral-clustering","title":"Spatially Constrained Spectral Clustering Algorithms for Region Delineation","date":"2019-05-21","arxiv_id":"1905.08451","n_code_links":0,"syntology":null},{"paper":"/paper/spectral-metric-for-dataset-complexity","slug":"spectral-metric-for-dataset-complexity","title":"Spectral Metric for Dataset Complexity Assessment","date":"2019-05-17","arxiv_id":"1905.07299","n_code_links":1,"syntology":null},{"paper":null,"slug":"easics-the-objective-and-fine-grained","title":"EasiCS: the objective and fine-grained classification method of cervical spondylosis dysfunction","date":"2019-05-15","arxiv_id":"1905.05987","n_code_links":0,"syntology":null},{"paper":null,"slug":"spectral-clustering-of-signed-graphs-via","title":"Spectral Clustering of Signed Graphs via Matrix Power Means","date":"2019-05-15","arxiv_id":"1905.06230","n_code_links":0,"syntology":null},{"paper":"/paper/correlated-variational-auto-encoders","slug":"correlated-variational-auto-encoders","title":"Correlated Variational Auto-Encoders","date":"2019-05-14","arxiv_id":"1905.05335","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["datang1992/Correlated-VAEs"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"joint-learning-of-self-representation-and","title":"Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering","date":"2019-05-11","arxiv_id":"1905.04432","n_code_links":0,"syntology":null},{"paper":null,"slug":"three-stage-subspace-clustering-framework","title":"Three-Stage Subspace Clustering Framework with Graph-Based Transformation and Optimization","date":"2019-05-02","arxiv_id":"1905.01145","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-classification-into-unknown","title":"Unsupervised classification into unknown number of classes","date":"2019-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-spectral-clustering-using-dual","title":"Deep Spectral Clustering using Dual Autoencoder Network","date":"2019-04-30","arxiv_id":"1904.13113","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-subspace-clustering-by-cauchy-loss","title":"Robust subspace clustering by Cauchy loss function","date":"2019-04-28","arxiv_id":"1904.12274","n_code_links":0,"syntology":null},{"paper":null,"slug":"190412605","title":"N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network","date":"2019-04-12","arxiv_id":"1904.12605","n_code_links":0,"syntology":null},{"paper":null,"slug":"3d-quantum-cuts-for-automatic-segmentation-of","title":"3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images","date":"2019-04-09","arxiv_id":"1904.04412","n_code_links":0,"syntology":null},{"paper":"/paper/simultaneous-dimensionality-and-complexity","slug":"simultaneous-dimensionality-and-complexity","title":"Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering","date":"2019-04-05","arxiv_id":"1904.02926","n_code_links":1,"syntology":null},{"paper":"/paper/community-detection-over-a-heterogeneous","slug":"community-detection-over-a-heterogeneous","title":"Community detection over a heterogeneous population of non-aligned networks","date":"2019-04-04","arxiv_id":"1904.05332","n_code_links":1,"syntology":null},{"paper":"/paper/learning-for-multi-type-subspace-clustering","slug":"learning-for-multi-type-subspace-clustering","title":"Learning for Multi-Type Subspace Clustering","date":"2019-04-03","arxiv_id":"1904.02075","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-efficient-approach-with-data-adaptive","title":"A Novel Efficient Approach with Data-Adaptive Capability for OMP-based Sparse Subspace Clustering","date":"2019-03-05","arxiv_id":"1903.01734","n_code_links":0,"syntology":null},{"paper":"/paper/ultra-scalable-spectral-clustering-and","slug":"ultra-scalable-spectral-clustering-and","title":"Ultra-Scalable Spectral Clustering and Ensemble Clustering","date":"2019-03-04","arxiv_id":"1903.01057","n_code_links":0,"syntology":null},{"paper":null,"slug":"going-deep-in-clustering-high-dimensional","title":"Deep Mixtures of Unigrams for uncovering Topics in Textual Data","date":"2019-02-18","arxiv_id":"1902.06615","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-automated-spectral-clustering-for-multi","title":"An Automated Spectral Clustering for Multi-scale Data","date":"2019-02-06","arxiv_id":"1902.01990","n_code_links":0,"syntology":null},{"paper":null,"slug":"geometric-structure-of-graph-laplacian","title":"Geometric structure of graph Laplacian embeddings","date":"2019-01-30","arxiv_id":"1901.10651","n_code_links":0,"syntology":null},{"paper":"/paper/deep-constrained-clustering-algorithms-and","slug":"deep-constrained-clustering-algorithms-and","title":"A Framework for Deep Constrained Clustering -- Algorithms and Advances","date":"2019-01-29","arxiv_id":"1901.10061","n_code_links":1,"syntology":null},{"paper":null,"slug":"optimized-deformed-laplacian-for-spectrum","title":"Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs","date":"2019-01-25","arxiv_id":"1901.09715","n_code_links":0,"syntology":null},{"paper":"/paper/guarantees-for-spectral-clustering-with","slug":"guarantees-for-spectral-clustering-with","title":"Guarantees for Spectral Clustering with Fairness Constraints","date":"2019-01-24","arxiv_id":"1901.08668","n_code_links":1,"syntology":null},{"paper":null,"slug":"spectral-clustering-via-ensemble-deep","title":"Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE)","date":"2019-01-08","arxiv_id":"1901.02291","n_code_links":0,"syntology":null},{"paper":null,"slug":"mixture-learning-from-partial-observations","title":"Learning RUMs: Reducing Mixture to Single Component via PCA","date":"2018-12-31","arxiv_id":"1812.11917","n_code_links":0,"syntology":null},{"paper":"/paper/mixed-order-spectral-clustering-for-networks","slug":"mixed-order-spectral-clustering-for-networks","title":"Mixed-Order Spectral Clustering for Networks","date":"2018-12-25","arxiv_id":"1812.10140","n_code_links":1,"syntology":null},{"paper":null,"slug":"connecting-spectral-clustering-to-maximum","title":"Connecting Spectral Clustering to Maximum Margins and Level Sets","date":"2018-12-16","arxiv_id":"1812.06397","n_code_links":0,"syntology":null},{"paper":null,"slug":"higher-order-spectral-clustering-under","title":"Higher-Order Spectral Clustering under Superimposed Stochastic Block Model","date":"2018-12-16","arxiv_id":"1812.06515","n_code_links":0,"syntology":null},{"paper":null,"slug":"image-segmentation-based-on-multiscale-fast","title":"Image Segmentation Based on Multiscale Fast Spectral Clustering","date":"2018-12-12","arxiv_id":"1812.04816","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-learning-local-supervision-encoding","title":"Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM","date":"2018-12-05","arxiv_id":"1812.01967","n_code_links":0,"syntology":null},{"paper":"/paper/spectral-feature-transformation-for-person-re","slug":"spectral-feature-transformation-for-person-re","title":"Spectral Feature Transformation for Person Re-identification","date":"2018-11-28","arxiv_id":"1811.11405","n_code_links":2,"syntology":null},{"paper":"/paper/subspace-clustering-through-sub-clusters","slug":"subspace-clustering-through-sub-clusters","title":"Subspace Clustering through Sub-Clusters","date":"2018-11-15","arxiv_id":"1811.06580","n_code_links":1,"syntology":null},{"paper":"/paper/spectral-embedding-norm-looking-deep-into-the","slug":"spectral-embedding-norm-looking-deep-into-the","title":"Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian","date":"2018-10-25","arxiv_id":"1810.10695","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-by-unsupervised-nonlinear-diffusion","title":"Learning by Unsupervised Nonlinear Diffusion","date":"2018-10-15","arxiv_id":"1810.06702","n_code_links":0,"syntology":null},{"paper":null,"slug":"identification-of-invariant-sensorimotor","title":"Identification of Invariant Sensorimotor Structures as a Prerequisite for the Discovery of Objects","date":"2018-10-11","arxiv_id":"1810.05057","n_code_links":0,"syntology":null},{"paper":null,"slug":"hierarchical-community-detection-by-recursive","title":"Hierarchical community detection by recursive partitioning","date":"2018-10-02","arxiv_id":"1810.01509","n_code_links":0,"syntology":null},{"paper":null,"slug":"vector-quantized-spectral-clustering-applied","title":"Vector Quantized Spectral Clustering applied to Soybean Whole Genome Sequences","date":"2018-09-30","arxiv_id":"1810.00398","n_code_links":0,"syntology":null},{"paper":null,"slug":"clustering-of-graph-vertex-subset-via-krylov","title":"Distance preserving model order reduction of graph-Laplacians and cluster analysis","date":"2018-09-09","arxiv_id":"1809.03048","n_code_links":0,"syntology":null},{"paper":"/paper/the-eurecom-submission-to-the-first-dihard","slug":"the-eurecom-submission-to-the-first-dihard","title":"The EURECOM Submission to the First DIHARD Challenge","date":"2018-09-06","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"computing-word-classes-using-spectral","title":"Computing Word Classes Using Spectral Clustering","date":"2018-08-16","arxiv_id":"1808.05374","n_code_links":0,"syntology":null},{"paper":null,"slug":"essential-tensor-learning-for-multi-view","title":"Essential Tensor Learning for Multi-view Spectral Clustering","date":"2018-07-10","arxiv_id":"1807.03602","n_code_links":0,"syntology":null},{"paper":null,"slug":"pairwise-covariates-adjusted-block-model-for","title":"Pairwise Covariates-adjusted Block Model for Community Detection","date":"2018-07-10","arxiv_id":"1807.03469","n_code_links":0,"syntology":null},{"paper":null,"slug":"certifying-global-optimality-of-graph-cuts","title":"Certifying Global Optimality of Graph Cuts via Semidefinite Relaxation: A Performance Guarantee for Spectral Clustering","date":"2018-06-29","arxiv_id":"1806.11429","n_code_links":0,"syntology":null},{"paper":null,"slug":"scsp-spectral-clustering-filter-pruning-with","title":"SCSP: Spectral Clustering Filter Pruning with Soft Self-adaption Manners","date":"2018-06-14","arxiv_id":"1806.05320","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-regularized-spectral-clustering","slug":"understanding-regularized-spectral-clustering","title":"Understanding Regularized Spectral Clustering via Graph Conductance","date":"2018-06-05","arxiv_id":"1806.01468","n_code_links":1,"syntology":null},{"paper":null,"slug":"adversarial-confidence-and-smoothness","title":"Adversarial confidence and smoothness regularizations for scalable unsupervised discriminative learning","date":"2018-06-04","arxiv_id":"1806.00919","n_code_links":0,"syntology":null},{"paper":"/paper/large-scale-spectral-clustering-using","slug":"large-scale-spectral-clustering-using","title":"Large-scale spectral clustering using diffusion coordinates on landmark-based bipartite graphs","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"local-and-global-optimization-techniques-in","title":"Local and Global Optimization Techniques in Graph-Based Clustering","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/scalable-spectral-clustering-using-random","slug":"scalable-spectral-clustering-using-random","title":"Scalable Spectral Clustering Using Random Binning Features","date":"2018-05-25","arxiv_id":"1805.11048","n_code_links":1,"syntology":null},{"paper":null,"slug":"hypergraph-spectral-clustering-in-the","title":"Hypergraph Spectral Clustering in the Weighted Stochastic Block Model","date":"2018-05-23","arxiv_id":"1805.08956","n_code_links":0,"syntology":null},{"paper":null,"slug":"convex-programming-based-spectral-clustering","title":"Convex Programming Based Spectral Clustering","date":"2018-05-11","arxiv_id":"1805.04246","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-sparse-non-negative-matrix-factorization","title":"A Sparse Non-negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior from MRI","date":"2018-04-15","arxiv_id":"1804.05370","n_code_links":0,"syntology":null},{"paper":"/paper/multi-view-banded-spectral-clustering-with","slug":"multi-view-banded-spectral-clustering-with","title":"Multi-view Banded Spectral Clustering with Application to ICD9 Clustering","date":"2018-04-06","arxiv_id":"1804.02097","n_code_links":1,"syntology":null},{"paper":null,"slug":"fast-subspace-clustering-based-on-the","title":"Fast Subspace Clustering Based on the Kronecker Product","date":"2018-03-15","arxiv_id":"1803.05657","n_code_links":0,"syntology":null},{"paper":null,"slug":"analysis-of-spectral-clustering-algorithms","title":"Analysis of spectral clustering algorithms for community detection: the general bipartite setting","date":"2018-03-12","arxiv_id":"1803.04547","n_code_links":0,"syntology":null},{"paper":null,"slug":"scalable-and-robust-sparse-subspace","title":"Scalable and Robust Sparse Subspace Clustering Using Randomized Clustering and Multilayer Graphs","date":"2018-02-21","arxiv_id":"1802.07648","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-minimax-misclassification-ratio-of","title":"On the Minimax Misclassification Ratio of Hypergraph Community Detection","date":"2018-02-03","arxiv_id":"1802.00926","n_code_links":0,"syntology":null},{"paper":"/paper/spectralnet-spectral-clustering-using-deep","slug":"spectralnet-spectral-clustering-using-deep","title":"SpectralNet: Spectral Clustering using Deep Neural Networks","date":"2018-01-04","arxiv_id":"1801.01587","n_code_links":3,"syntology":null},{"paper":"/paper/path-based-spectral-clustering-guarantees","slug":"path-based-spectral-clustering-guarantees","title":"Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms","date":"2017-12-17","arxiv_id":"1712.06206","n_code_links":1,"syntology":null},{"paper":null,"slug":"incremental-eigenpair-computation-for-graph","title":"Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications","date":"2017-12-13","arxiv_id":"1801.08196","n_code_links":0,"syntology":null},{"paper":null,"slug":"nonconvex-sparse-spectral-clustering-by","title":"Nonconvex Sparse Spectral Clustering by Alternating Direction Method of Multipliers and Its Convergence Analysis","date":"2017-12-08","arxiv_id":"1712.02979","n_code_links":0,"syntology":null},{"paper":"/paper/quantum-transport-senses-community-structure","slug":"quantum-transport-senses-community-structure","title":"Quantum transport senses community structure in networks","date":"2017-11-14","arxiv_id":"1711.04979","n_code_links":1,"syntology":null},{"paper":null,"slug":"marginalized-graph-autoencoder-for-graph","title":"Marginalized graph autoencoder for graph clustering","date":"2017-11-06","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"kernel-k-groups-via-hartigans-method","title":"Kernel k-Groups via Hartigan's Method","date":"2017-10-26","arxiv_id":"1710.09859","n_code_links":0,"syntology":null}],"record_sha256":"4e6b5f146d1ef4c770f9d5be9e0bf5d4bb455ca2f648341175f246546c2fc6ec","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}