A survey of kernel and spectral methods for clustering

By Maurizio Filippone, Francesco Camastra, Francesco Masulli, Stefano Rovetta. Pattern Recognition, 2008

911 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/052cbe80c4e99e6070df432aa8e462a966f56e49

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering (2012, neutral): “[24]).”
k-Shape: Efficient and Accurate Clustering of Time Series (2015, background): “Spectral clustering [58] has recently started receiving attention [7] due to its success over other types of data [21].”
Multi-view low-rank sparse subspace clustering (2017, methodology): “Spectral based methods [11, 12, 13] present one approach to subspace clustering problem.”
A Comprehensive Survey of Clustering Algorithms (2015, background): “For more detailed information about this kind of clustering algorithms, you can refer to [82–84].”
Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study (2010, neutral): “[3,5,6,8,10,11,15,16,23,25,27–32].”
Kernel Methods and Machine Learning (2014, background): “For a general survey, as well as more useful references, see [67].”
Multiple Kernel Fuzzy Clustering (2012, background): “Filippone et al. [25] contributed a survey of kernel and spectral clustering methods.”
A Nonlinear Orthogonal Non-Negative Matrix Factorization Approach to Subspace Clustering (2017, methodology): “19 Spectral clustering can be seen as a graph partition problem and solved by the eigenvalue decom20 position of the graph Laplacian matrix [18, 19, 20, 21, 22].”
A random walk in the literature on criminality: A partial and critical view on some statistical analyses and modelling approaches (2010, neutral): “mostly studied by the community of machine learning research [6, 40, 57, 106].”
k-Shape: Efficient and Accurate Clustering of Time Series (2016, background): “Spectral clustering [49] has recently started receiving attention [6] due to its success over other types of data [17].”
Spectral Clustering for Speech Separation (2009, methodology): “For similar clustering approaches based on kernel methods, see Filippone et al. (2008), Bie and Cristianini (2006), Bach and Harchaoui (2008).”
Large Scale Spectral Clustering with Landmark-Based Representation (2011, background): “…clustering algorithms are based on Euclidean geometry and consequently place limitations on the shape of the clusters, spectral clustering can adapt to a wider range of geometries and detect non-convex patterns and linearly non-separable clusters (Ng, Jordan, and Weiss 2001; Filippone et al. 2008).”
Large Scale Spectral Clustering Via Landmark-Based Sparse Representation (2015, background): “While many clustering algorithms are based on Euclidean geometry and consequently place limitations on the shape of the clusters, spectral clustering can adapt to a wider range of geometries and detect nonconvex patterns and linearly nonseparable clusters [13], [31].”