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Subspace Clustering via Thresholding and Spectral Clustering
Subspace Clustering Thresholding Spectral Clustering
2013/5/2
We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown...
Epidemic diffusion on a graph is a dynamic process that transitions simultaneously to all of a node's neighbors, in contrast to a random walk, which selects only a single neighbor. Epidemic diffusion ...
Multiway Spectral Clustering: A Margin-Based Perspective
Spectral clustering spectral relaxation graph partitioning reproducing kernel Hilbert space large-margin classifi ca-tion Gaussian intrinsic autoregression
2011/3/22
Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is "relaxed" into a tractable eigenvector problem, and in whi...
Multiway Spectral Clustering: A Margin-Based Perspective
Spectral clustering spectral relaxation graph partitioning reproducing kernel Hilbert space large-margin classifi ca-tion Gaussian intrinsic autoregression
2011/3/23
Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is "relaxed" into a tractable eigenvector problem, and in whi...
An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA
Learning (cs.LG) Optimization and Control (math.OC) Machine Learning (stat.ML)
2010/12/17
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors amounts to finding cri...
Operator norm convergence of spectral clustering on level sets
Spectral clustering graph unsupervised classification levelsets connected components
2010/3/10
Following Hartigan [1975], a cluster is defined as a connected component of
the t-level set of the underlying density, i.e., the set of points for which the
density is greater than t. A clustering a...
Spectral clustering based on local linear approximations
Spectral Clustering Higher-Order Affinities Local Linear Approximation Local PolynomialApproximation
2010/3/9
In the context of clustering, we assume a generative model where each cluster is the result
of sampling points in the neighborhood of an embedded smooth surface, possibly contaminated
with outliers....