{"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/5","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":5,"pages_in_order":6,"rows_per_page":100,"rows":[401,500],"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/4","next":"/method/spectral-clustering/papers/6","papers":[{"paper":null,"slug":"towards-scalable-spectral-clustering-via","title":"Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification","date":"2017-10-12","arxiv_id":"1710.04584","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-self-balanced-min-cut-algorithm-for-image","title":"A Self-Balanced Min-Cut Algorithm for Image Clustering","date":"2017-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-action-discovery-and","title":"Unsupervised Action Discovery and Localization in Videos","date":"2017-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a-nonlinear-orthogonal-non-negative-matrix","slug":"a-nonlinear-orthogonal-non-negative-matrix","title":"A Nonlinear Orthogonal Non-Negative Matrix Factorization Approach to Subspace Clustering","date":"2017-09-29","arxiv_id":"1709.10323","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-view-spectral-clustering-via-structured","title":"Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization","date":"2017-09-05","arxiv_id":"1709.01212","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-compressive-sensing-approach-to-community","title":"A Compressive Sensing Approach to Community Detection with Applications","date":"2017-08-30","arxiv_id":"1708.09477","n_code_links":0,"syntology":null},{"paper":"/paper/multi-view-low-rank-sparse-subspace","slug":"multi-view-low-rank-sparse-subspace","title":"Multi-view Low-rank Sparse Subspace Clustering","date":"2017-08-29","arxiv_id":"1708.08732","n_code_links":2,"syntology":null},{"paper":"/paper/discovering-political-topics-in-facebook","slug":"discovering-political-topics-in-facebook","title":"Discovering Political Topics in Facebook Discussion threads with Graph Contextualization","date":"2017-08-23","arxiv_id":"1708.06872","n_code_links":2,"syntology":null},{"paper":null,"slug":"preconditioned-spectral-clustering-for","title":"Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge","date":"2017-08-21","arxiv_id":"1708.07481","n_code_links":0,"syntology":null},{"paper":null,"slug":"innovation-pursuit-a-new-approach-to-the","title":"Innovation Pursuit: A New Approach to the Subspace Clustering Problem","date":"2017-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-robust-representations-for-computer","title":"Learning Robust Representations for Computer Vision","date":"2017-07-31","arxiv_id":"1708.00069","n_code_links":0,"syntology":null},{"paper":null,"slug":"grassmannian-manifold-optimization-assisted","title":"Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering","date":"2017-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"semantic-word-clusters-using-signed-spectral","title":"Semantic Word Clusters Using Signed Spectral Clustering","date":"2017-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"iterative-spectral-clustering-for","title":"Iterative Spectral Clustering for Unsupervised Object Localization","date":"2017-06-29","arxiv_id":"1706.09719","n_code_links":0,"syntology":null},{"paper":null,"slug":"face-clustering-representation-and-pairwise","title":"Face Clustering: Representation and Pairwise Constraints","date":"2017-06-15","arxiv_id":"1706.05067","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-approximate-spectral-clustering-for","title":"Fast Approximate Spectral Clustering for Dynamic Networks","date":"2017-06-12","arxiv_id":"1706.03591","n_code_links":0,"syntology":null},{"paper":"/paper/clustering-with-t-sne-provably","slug":"clustering-with-t-sne-provably","title":"Clustering with t-SNE, provably","date":"2017-06-08","arxiv_id":"1706.02582","n_code_links":2,"syntology":null},{"paper":null,"slug":"ensemble-of-part-detectors-for-simultaneous","title":"Ensemble of Part Detectors for Simultaneous Classification and Localization","date":"2017-05-29","arxiv_id":"1705.10034","n_code_links":0,"syntology":null},{"paper":null,"slug":"online-factorization-and-partition-of-complex","title":"Online Factorization and Partition of Complex Networks From Random Walks","date":"2017-05-22","arxiv_id":"1705.07881","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-similarities-in-epileptic","title":"Identifying Similarities in Epileptic Patients for Drug Resistance Prediction","date":"2017-04-26","arxiv_id":"1704.08361","n_code_links":0,"syntology":null},{"paper":null,"slug":"spectral-and-matrix-factorization-methods-for","title":"Spectral and matrix factorization methods for consistent community detection in multi-layer networks","date":"2017-04-24","arxiv_id":"1704.07353","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-spectral-clustering-using-autoencoders","title":"Fast Spectral Clustering Using Autoencoders and Landmarks","date":"2017-04-07","arxiv_id":"1704.02345","n_code_links":0,"syntology":null},{"paper":null,"slug":"novel-framework-for-spectral-clustering-using","title":"Novel Framework for Spectral Clustering using Topological Node Features(TNF)","date":"2017-03-31","arxiv_id":"1703.10756","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-spectral-clustering-using-the","title":"Improving Spectral Clustering using the Asymptotic Value of the Normalised Cut","date":"2017-03-29","arxiv_id":"1703.09975","n_code_links":0,"syntology":null},{"paper":"/paper/graph-sketching-based-space-efficient-data","slug":"graph-sketching-based-space-efficient-data","title":"Graph sketching-based Space-efficient Data Clustering","date":"2017-03-07","arxiv_id":"1703.02375","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-consistency-of-compressive-spectral","title":"On Consistency of Compressive Spectral Clustering","date":"2017-02-12","arxiv_id":"1702.03522","n_code_links":0,"syntology":null},{"paper":"/paper/clustering-signed-networks-with-the-geometric","slug":"clustering-signed-networks-with-the-geometric","title":"Clustering Signed Networks with the Geometric Mean of Laplacians","date":"2017-01-03","arxiv_id":"1701.00757","n_code_links":1,"syntology":null},{"paper":null,"slug":"human-action-attribute-learning-from-video","title":"Human Action Attribute Learning From Video Data Using Low-Rank Representations","date":"2016-12-23","arxiv_id":"1612.07857","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-local-scaling-using-conditional","title":"Robust Local Scaling using Conditional Quantiles of Graph Similarities","date":"2016-12-14","arxiv_id":"1612.04875","n_code_links":0,"syntology":null},{"paper":null,"slug":"noisy-subspace-clustering-via-matching","title":"Noisy subspace clustering via matching pursuits","date":"2016-12-11","arxiv_id":"1612.03450","n_code_links":0,"syntology":null},{"paper":"/paper/general-tensor-spectral-co-clustering-for","slug":"general-tensor-spectral-co-clustering-for","title":"General Tensor Spectral Co-clustering for Higher-Order Data","date":"2016-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/node-embedding-via-word-embedding-for-network","slug":"node-embedding-via-word-embedding-for-network","title":"Node Embedding via Word Embedding for Network Community Discovery","date":"2016-11-09","arxiv_id":"1611.03028","n_code_links":1,"syntology":null},{"paper":null,"slug":"retrieving-challenging-vessel-connections-in","title":"Retrieving challenging vessel connections in retinal images by line co-occurrence statistics","date":"2016-10-20","arxiv_id":"1610.06368","n_code_links":0,"syntology":null},{"paper":null,"slug":"structured-sparse-subspace-clustering-a-joint","title":"Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework","date":"2016-10-17","arxiv_id":"1610.05211","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-clustering-under-the-union-of","title":"Unsupervised clustering under the Union of Polyhedral Cones (UOPC) model","date":"2016-10-15","arxiv_id":"1610.04751","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-and-effective-algorithms-for-symmetric","title":"Fast and Effective Algorithms for Symmetric Nonnegative Matrix Factorization","date":"2016-09-17","arxiv_id":"1609.05342","n_code_links":0,"syntology":null},{"paper":null,"slug":"reconstructing-articulated-rigged-models-from","title":"Reconstructing Articulated Rigged Models from RGB-D Videos","date":"2016-09-06","arxiv_id":"1609.01371","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-novel-approach-for-shot-boundary-detection","title":"A Novel Approach for Shot Boundary Detection in Videos","date":"2016-08-24","arxiv_id":"1608.06716","n_code_links":0,"syntology":null},{"paper":null,"slug":"mini-batch-spectral-clustering","title":"Mini-Batch Spectral Clustering","date":"2016-07-07","arxiv_id":"1607.02024","n_code_links":0,"syntology":null},{"paper":null,"slug":"instance-level-video-segmentation-from-object","title":"Instance-Level Video Segmentation From Object Tracks","date":"2016-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"option-discovery-in-hierarchical","title":"Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering","date":"2016-05-17","arxiv_id":"1605.05359","n_code_links":0,"syntology":null},{"paper":null,"slug":"detecting-optional-arguments-of-verbs","title":"Detecting Optional Arguments of Verbs","date":"2016-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"studying-the-temporal-dynamics-of-word-co","title":"Studying the Temporal Dynamics of Word Co-occurrences: An Application to Event Detection","date":"2016-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"weighted-spectral-cluster-ensemble","title":"Weighted Spectral Cluster Ensemble","date":"2016-04-25","arxiv_id":"1604.07178","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-source-multi-view-clustering-via","title":"Multi-Source Multi-View Clustering via Discrepancy Penalty","date":"2016-04-14","arxiv_id":"1604.04029","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-clustering-and-graph-partitioning-via","title":"Data Clustering and Graph Partitioning via Simulated Mixing","date":"2016-03-15","arxiv_id":"1603.04918","n_code_links":0,"syntology":null},{"paper":null,"slug":"regression-based-hypergraph-learning-for","title":"Regression-based Hypergraph Learning for Image Clustering and Classification","date":"2016-03-14","arxiv_id":"1603.04150","n_code_links":0,"syntology":null},{"paper":null,"slug":"incremental-method-for-spectral-clustering-of","title":"Incremental Method for Spectral Clustering of Increasing Orders","date":"2015-12-23","arxiv_id":"1512.07349","n_code_links":0,"syntology":null},{"paper":null,"slug":"analysis-of-vessel-connectivities-in-retinal","title":"Analysis of Vessel Connectivities in Retinal Images by Cortically Inspired Spectral Clustering","date":"2015-12-21","arxiv_id":"1512.06559","n_code_links":0,"syntology":null},{"paper":null,"slug":"innovation-pursuit-a-new-approach-to-subspace","title":"Innovation Pursuit: A New Approach to Subspace Clustering","date":"2015-12-02","arxiv_id":"1512.00907","n_code_links":0,"syntology":null},{"paper":null,"slug":"motion-trajectory-segmentation-via-minimum","title":"Motion Trajectory Segmentation via Minimum Cost Multicuts","date":"2015-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-consistency-of-common-neighbors-for-link","title":"The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels","date":"2015-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"weakly-supervised-graph-based-semantic","title":"Weakly Supervised Graph Based Semantic Segmentation by Learning Communities of Image-Parts","date":"2015-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"convex-sparse-spectral-clustering-single-view","title":"Convex Sparse Spectral Clustering: Single-view to Multi-view","date":"2015-11-21","arxiv_id":"1511.06860","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-a-tracking-algorithm-based-on-the","title":"Towards a tracking algorithm based on the clustering of spatio-temporal clouds of points","date":"2015-11-04","arxiv_id":"1511.01293","n_code_links":0,"syntology":null},{"paper":null,"slug":"probably-certifiably-correct-k-means","title":"Probably certifiably correct k-means clustering","date":"2015-09-26","arxiv_id":"1509.07983","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-collusion-groups-using-spectral","title":"Identifying collusion groups using spectral clustering","date":"2015-09-22","arxiv_id":"1509.06457","n_code_links":0,"syntology":null},{"paper":"/paper/deep-clustering-discriminative-embeddings-for","slug":"deep-clustering-discriminative-embeddings-for","title":"Deep clustering: Discriminative embeddings for segmentation and separation","date":"2015-08-18","arxiv_id":"1508.04306","n_code_links":8,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"spectral-clustering-and-block-models-a-review","title":"Spectral Clustering and Block Models: A Review And A New Algorithm","date":"2015-08-07","arxiv_id":"1508.01819","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-decision-forest-for-data","title":"Unsupervised Decision Forest for Data Clustering and Density Estimation","date":"2015-07-15","arxiv_id":"1507.04060","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-review-of-nonnegative-matrix-factorization","title":"A Review of Nonnegative Matrix Factorization Methods for Clustering","date":"2015-07-12","arxiv_id":"1507.03194","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-spectral-method-for-community-detection-in","title":"A spectral method for community detection in moderately-sparse degree-corrected stochastic block models","date":"2015-06-29","arxiv_id":"1506.08621","n_code_links":0,"syntology":null},{"paper":null,"slug":"detectability-thresholds-and-optimal","title":"Detectability thresholds and optimal algorithms for community structure in dynamic networks","date":"2015-06-19","arxiv_id":"1506.06179","n_code_links":0,"syntology":null},{"paper":null,"slug":"structured-sparse-subspace-clustering-a","title":"Structured Sparse Subspace Clustering: A Unified Optimization Framework","date":"2015-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"superpixel-segmentation-using-linear-spectral","title":"Superpixel Segmentation Using Linear Spectral Clustering","date":"2015-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"parallel-spectral-clustering-algorithm-based","title":"Parallel Spectral Clustering Algorithm Based on Hadoop","date":"2015-05-31","arxiv_id":"1506.00227","n_code_links":0,"syntology":null},{"paper":null,"slug":"constrained-1-spectral-clustering","title":"Constrained 1-Spectral Clustering","date":"2015-05-24","arxiv_id":"1505.06485","n_code_links":0,"syntology":null},{"paper":null,"slug":"kernel-spectral-clustering-and-applications","title":"Kernel Spectral Clustering and applications","date":"2015-05-03","arxiv_id":"1505.00477","n_code_links":0,"syntology":null},{"paper":null,"slug":"phase-transitions-in-spectral-community","title":"Phase Transitions in Spectral Community Detection of Large Noisy Networks","date":"2015-04-09","arxiv_id":"1504.02412","n_code_links":0,"syntology":null},{"paper":null,"slug":"spectral-clustering-by-ellipsoid-and-its","title":"Spectral Clustering by Ellipsoid and Its Connection to Separable Nonnegative Matrix Factorization","date":"2015-03-05","arxiv_id":"1503.01531","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-review-of-mean-shift-algorithms-for","title":"A review of mean-shift algorithms for clustering","date":"2015-03-02","arxiv_id":"1503.00687","n_code_links":0,"syntology":null},{"paper":null,"slug":"correlation-adaptive-subspace-segmentation-by","title":"Correlation Adaptive Subspace Segmentation by Trace Lasso","date":"2015-01-18","arxiv_id":"1501.04276","n_code_links":0,"syntology":null},{"paper":null,"slug":"cross-document-co-reference-resolution-using","title":"Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment","date":"2015-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"a-convex-formulation-for-spectral-shrunk","title":"A Convex Formulation for Spectral Shrunk Clustering","date":"2014-11-23","arxiv_id":"1411.6308","n_code_links":0,"syntology":null},{"paper":null,"slug":"improved-spectral-clustering-via-embedded","title":"Improved Spectral Clustering via Embedded Label Propagation","date":"2014-11-23","arxiv_id":"1411.6241","n_code_links":0,"syntology":null},{"paper":null,"slug":"clustering-evolving-data-using-kernel-based","title":"Clustering evolving data using kernel-based methods","date":"2014-11-20","arxiv_id":"1411.5988","n_code_links":0,"syntology":null},{"paper":null,"slug":"covariate-assisted-spectral-clustering","title":"Covariate-assisted spectral clustering","date":"2014-11-08","arxiv_id":"1411.2158","n_code_links":0,"syntology":null},{"paper":null,"slug":"partitioning-well-clustered-graphs-spectral","title":"Partitioning Well-Clustered Graphs: Spectral Clustering Works!","date":"2014-11-07","arxiv_id":"1411.2021","n_code_links":0,"syntology":null},{"paper":null,"slug":"backhaul-constrained-multi-cell-cooperation","title":"Backhaul-Constrained Multi-Cell Cooperation Leveraging Sparsity and Spectral Clustering","date":"2014-09-30","arxiv_id":"1409.8359","n_code_links":0,"syntology":null},{"paper":null,"slug":"cortical-spatio-temporal-dimensionality","title":"Cortical spatio-temporal dimensionality reduction for visual grouping","date":"2014-07-02","arxiv_id":"1407.0733","n_code_links":0,"syntology":null},{"paper":null,"slug":"semi-supervised-spectral-clustering-for-image","title":"Semi-supervised Spectral Clustering for Image Set Classification","date":"2014-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"spectral-graph-reduction-for-efficient-image","title":"Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation","date":"2014-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"transitive-distance-clustering-with-k-means","title":"Transitive Distance Clustering with K-Means Duality","date":"2014-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"the-geometry-of-kernelized-spectral","title":"The geometry of kernelized spectral clustering","date":"2014-04-29","arxiv_id":"1404.7552","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-and-computationally-feasible-community","title":"Robust and computationally feasible community detection in the presence of arbitrary outlier nodes","date":"2014-04-23","arxiv_id":"1404.6000","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-comparison-of-clustering-and-missing-data","title":"A Comparison of Clustering and Missing Data Methods for Health Sciences","date":"2014-04-22","arxiv_id":"1404.5899","n_code_links":0,"syntology":null},{"paper":null,"slug":"spectral-sparse-representation-for-clustering","title":"Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut","date":"2014-03-25","arxiv_id":"1403.6290","n_code_links":0,"syntology":null},{"paper":null,"slug":"neighborhood-selection-for-thresholding-based","title":"Neighborhood Selection for Thresholding-based Subspace Clustering","date":"2014-03-13","arxiv_id":"1403.3438","n_code_links":0,"syntology":null},{"paper":null,"slug":"subspace-clustering-using-a-symmetric-low","title":"Subspace clustering using a symmetric low-rank representation","date":"2014-03-07","arxiv_id":"1403.2330","n_code_links":0,"syntology":null},{"paper":"/paper/the-hidden-convexity-of-spectral-clustering","slug":"the-hidden-convexity-of-spectral-clustering","title":"The Hidden Convexity of Spectral Clustering","date":"2014-03-04","arxiv_id":"1403.0667","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-semidefinite-spectral-clustering","title":"Efficient Semidefinite Spectral Clustering via Lagrange Duality","date":"2014-02-22","arxiv_id":"1402.5497","n_code_links":0,"syntology":null},{"paper":null,"slug":"active-clustering-with-model-based","title":"Active Clustering with Model-Based Uncertainty Reduction","date":"2014-02-07","arxiv_id":"1402.1783","n_code_links":0,"syntology":null},{"paper":null,"slug":"randomized-nonlinear-component-analysis","title":"Randomized Nonlinear Component Analysis","date":"2014-02-01","arxiv_id":"1402.0119","n_code_links":0,"syntology":null},{"paper":null,"slug":"context-aware-hypergraph-construction-for","title":"Context-Aware Hypergraph Construction for Robust Spectral Clustering","date":"2014-01-04","arxiv_id":"1401.0764","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-matrix-ridge-approximation-algorithms-and","title":"The Matrix Ridge Approximation: Algorithms and Applications","date":"2013-12-17","arxiv_id":"1312.4717","n_code_links":0,"syntology":null},{"paper":null,"slug":"consistency-of-spectral-clustering-in","title":"Consistency of spectral clustering in stochastic block models","date":"2013-12-07","arxiv_id":"1312.2050","n_code_links":0,"syntology":null},{"paper":null,"slug":"impact-of-regularization-on-spectral","title":"Impact of regularization on Spectral Clustering","date":"2013-12-05","arxiv_id":"1312.1733","n_code_links":0,"syntology":null},{"paper":null,"slug":"role-of-normalization-in-spectral-clustering","title":"Role of normalization in spectral clustering for stochastic blockmodels","date":"2013-10-05","arxiv_id":"1310.1495","n_code_links":0,"syntology":null},{"paper":null,"slug":"regularized-spectral-clustering-under-the","title":"Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel","date":"2013-09-16","arxiv_id":"1309.4111","n_code_links":0,"syntology":null},{"paper":"/paper/robust-subspace-clustering-via-thresholding","slug":"robust-subspace-clustering-via-thresholding","title":"Robust Subspace Clustering via Thresholding","date":"2013-07-18","arxiv_id":"1307.4891","n_code_links":1,"syntology":null}],"record_sha256":"b47a55117d9e3c03c281b37febfef9f760316b845ad309c4660861e078384c9c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}