Learning Spectral Clustering

By Francis R. Bach, Michael I. Jordan. Neural Information Processing Systems, 2003

546 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/32fad9f3b7ab236f326d6927c2965a01118e8e93

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Citations in the corpus

Label Informed Attributed Network Embedding (2017, background): “They target at generating low-dimensional manifolds which can model the nonlinear geometry of data, including Isomap [38], Laplacian Eigenmaps [4] and spectral techniques [3, 8].”
Weighted Graph Cuts without Eigenvectors A Multilevel Approach (2007, result): “In this paper, we generalize and extend recent results [8], [9], [10], [11] on connections between vector-based and graph-based clustering to provide a unifying mathematical connection between kernel k-means and graph clustering objectives.”
Manifold-based Similarity Adaptation for Label Propagation (2013, unknown): “, [1, 16]).”
Kernel k-means: spectral clustering and normalized cuts (2004, methodology): “Our work has been largely inspired by recent results on spectral clustering and relaxation methods presented in [10, 13, 1, 14].”
Kernelized Infomax Clustering (2005, background): “Barber and Agakov (2003); Agakov and Barber (2006)).”
Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison (2012, background): “Finally, we note that although the concept of eigengap has been exploited in many studies of kernel learning [2, 12, 1, 17], to the best of our knowledge, this is the first time it has been incorporated in the analysis for approximate large-scale kernel learning.”
Parallel Spectral Clustering in Distributed Systems (2011, background): “For example, we may retain only Sij, where j (or i) is among the t nearest neighbors of i (or jÞ [2], [28].”
Social big data: Recent achievements and new challenges (2015, background): “veloped [139–141] into more efficient versions from the state-of-theart algorithms [142,143].”
Blind One-microphone Speech Separation: A Spectral Learning Approach (2004, background): “That is, although two speakers might speak simultaneously, there is relatively little overlap in the time-frequency plane if the speakers are different [5, 4].”
Learning a Mahalanobis distance metric for data clustering and classification (2008, background): “The use of the learned distance metrics has been demonstrated in many real-word applications, including speech processing [48], visual representation [49], word categorization [12], face verification [50], medical image processing [51], video object classification [52], biological data processing [5”
EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking (2007, unknown): “In the online clustering stage, an enhanced version of spectral clustering algorithm [11] which can handel noise data is CHI 2007 Proceedings • Photo Sharing April 28-May 3, 2007 • San Jose, CA, USA”
Virtual Phone Discovery for Speech Synthesis Without Text (2019, unknown): “Spectral clustering depends on the eigen structure of similarity matrix to optimize the partition of the graph with higher similarity within a cluster and lower similarity across the clusters [25, 26].”
A survey of kernel and spectral methods for clustering (2008, unknown): “[20] [21] [22] [23].”
A tutorial on spectral clustering (2007, methodology): “Bach and Jordan (2004) use a more advanced post-processing of the eigenvectors.”