Results 11 to 20 of about 27,772 (256)
The Complexity of Sparse Tensor PCA
CoRR, 2021 ISBN ...
Choo, Davin, D'Orsi, Tommaso
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Sparse PCA: a Geometric Approach
J. Mach. Learn. Res., 2022 We consider the problem of maximizing the variance explained from a data matrix using orthogonal sparse principal components that have a support of fixed cardinality. While most existing methods focus on building principal components (PCs) iteratively through deflation, we propose GeoSPCA, a novel algorithm to build all PCs at once while satisfying the
Dimitris Bertsimas, Driss Lahlou Kitane
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Psychometrika, 2019 It is well known that the classical exploratory factor analysis (EFA) of data with more observations than variables has several types of indeterminacy. We study the factor indeterminacy and show some new aspects of this problem by considering EFA as a specific data matrix decomposition.
Eldén, Lars, Trendafilov, Nickolay
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Phase transitions in sparse PCA [PDF]
2015 IEEE International Symposium on Information Theory (ISIT), 2015 We study optimal estimation for sparse principal component analysis when the number of non-zero elements is small but on the same order as the dimension of the data. We employ approximate message passing (AMP) algorithm and its state evolution to analyze what is the information theoretically minimal mean-squared error and the one achieved by AMP in the
Thibault Lesieur +2 more
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Information-theoretically optimal sparse PCA [PDF]
2014 IEEE International Symposium on Information Theory, 2014 Sparse Principal Component Analysis (PCA) is a dimensionality reduction technique wherein one seeks a low-rank representation of a data matrix with additional sparsity constraints on the obtained representation. We consider two probabilistic formulations of sparse PCA: a spiked Wigner and spiked Wishart (or spiked covariance) model.
Yash Deshpande, Andrea Montanari
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Online Tensor Robust Principal Component Analysis
IEEE Access, 2022 Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on data vectors and cannot directly be applied to higher-order data arrays (e.g. video frames). In
Mohammad M. Salut, David V. Anderson
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Sparse PCA on fixed-rank matrices [PDF]
Mathematical Programming, 2022 Sparse PCA is the optimization problem obtained from PCA by adding a sparsity constraint on the principal components. Sparse PCA is NP-hard and hard to approximate even in the single-component case. In this paper we settle the computational complexity of sparse PCA with respect to the rank of the covariance matrix.
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BMC Bioinformatics, 2017 Background Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data.
Ziyi Li, Sandra E. Safo, Qi Long
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Clustering Algorithm for High-Dimensional Data Under New Dimensionality Reduc-tion Criteria
Jisuanji kexue yu tansuo, 2020 In order to solve the problem that principal component analysis (PCA) algorithm can??t deal with the reduction of clustering accuracy after high dimensional data reduction, a new attribute space concept is proposed.
WAN Jing, WU Fan, HE Yunbin, LI Song
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A Literature Review of (Sparse) Exponential Family PCA [PDF]
Journal of Statistical Theory and Practice, 2022 AbstractThis is a brief overview of the methodology around exponential family PCA. We revisit classic PCA methodology, and we focus on exponential family PCA due to its applicability on a number of distributions and hence a wide variety of problems.
Luke Smallman, Andreas Artemiou
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