Results 11 to 20 of about 25,940 (257)
ICT Express, 2023 To improve the acquisition speed and inbound capacity of the ground station in a burst direct-sequence (DS) spread-spectrum transmission system, an acquisition method based on a modified parallel code-phase acquisition (PCA) scheme is proposed. By taking
Chengyao Tang +4 more
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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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Sparse PCA With Multiple Components
CoRR, 2022 Sparse Principal Component Analysis (sPCA) is a cardinal technique for obtaining combinations of features, or principal components (PCs), that explain the variance of high-dimensional datasets in an interpretable manner. This involves solving a sparsity and orthogonality constrained convex maximization problem, which is extremely computationally ...
Ryan Cory-Wright, Jean Pauphilet
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Subexponential-Time Algorithms for Sparse PCA
Foundations of Computational Mathematics, 2023 We study the computational cost of recovering a unit-norm sparse principal component $x \in \mathbb{R}^n$ planted in a random matrix, in either the Wigner or Wishart spiked model (observing either $W + λxx^\top$ with $W$ drawn from the Gaussian orthogonal ensemble, or $N$ independent samples from $\mathcal{N}(0, I_n + βxx^\top)$, respectively).
Yunzi Ding +3 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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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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