{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/deep-learning-with-nonparametric-clustering","title":"Deep Learning with Nonparametric Clustering","arxiv_id":"1501.03084","date":"2015-01-13","proceeding":null,"authors":["Gang Chen"],"abstract":"Clustering is an essential problem in machine learning and data mining. One\nvital factor that impacts clustering performance is how to learn or design the\ndata representation (or features). Fortunately, recent advances in deep\nlearning can learn unsupervised features effectively, and have yielded state of\nthe art performance in many classification problems, such as character\nrecognition, object recognition and document categorization. However, little\nattention has been paid to the potential of deep learning for unsupervised\nclustering problems. In this paper, we propose a deep belief network with\nnonparametric clustering. As an unsupervised method, our model first leverages\nthe advantages of deep learning for feature representation and dimension\nreduction. Then, it performs nonparametric clustering under a maximum margin\nframework -- a discriminative clustering model and can be trained online\nefficiently in the code space. Lastly model parameters are refined in the deep\nbelief network. Thus, this model can learn features for clustering and infer\nmodel complexity in an unified framework. The experimental results show the\nadvantage of our approach over competitive baselines.","url_abs":"http://arxiv.org/abs/1501.03084v1","url_pdf":"http://arxiv.org/pdf/1501.03084v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-with-nonparametric-clustering","repo_url":"https://github.com/linqinghong/Deep-Clustering-Paper","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"nonparametric-clustering","task_name":"Nonparametric Clustering"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[{"method_slug":"deep-belief-network","method_name":"Deep Belief Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1501.03084","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}