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We introduce a novel algorithm that computes the $k$-sparse principal component of a positive semidefinite matrix $A$. Our algorithm is combinatorial and operates by examining a discrete set of specia...
The performance of principal component analysis (PCA) suffers badly in the presence of outliers. This paper proposes two novel approaches for robust PCA based on semidefinite programming.
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...
Ergodicity of PCA: Equivalence between Spatial and Temporal Mixing Conditions
probabilistic Cellular Automata Interacting Particle Systems Ergodicity Gibbs measure
2009/4/28
For a general attractive Probabilistic Cellular Automata on SZ^d, we prove that the (time-) convergence towards equilibrium of this Markovian parallel dynamics, exponentially fast in the uniform norm,...