An iterative coordinate descent algorithm to compute sparse low-rank approximations
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Abstract (arXiv)
In this paper, we describe a new algorithm to build a few sparse principal components from a given data matrix. Our approach does not explicitly create the covariance matrix of the data and can be viewed as an extension of the Kogbetliantz algorithm to build an approximate singular value decomposition for a few principal components. We show the performance of the proposed algorithm to recover sparse principal components on various datasets from the literature and perform dimensionality reduction for classification applications.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 43 references that are in the map. First posted on arXiv 30 Jul 2021.
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References · 8
- 2014On the Computational Intractability of Exact and Approximate Dictionary Learningcited by 14
- 2014Sparse Generalized Eigenvalue Problem via Smooth Optimizationcited by 6
- 2016Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulationcited by 5
- 2016Learning Fast Sparsifying Transformscited by 4
- 2019Multi-Rank Sparse and Functional PCA: Manifold Optimization and Iterative Deflation Techniquescited by 3
- 2019A Dictionary-Based Generalization of Robust PCA Part II: Applications to Hyperspectral Demixingcited by 1
- 2019Fast approximation of orthogonal matrices and application to PCAcited by 1
- 2020Constructing fast approximate eigenspaces with application to the fast graph Fourier transformscited by 1
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