Results 11 to 20 of about 90,827 (166)
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
Krzakala, Florent +2 more
core +3 more sources
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
Deshpande, Yash, Montanari, Andrea
core +2 more sources
Sparse PCA: Optimal rates and adaptive estimation [PDF]
The Annals of Statistics, 2013 Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications. This paper considers both minimax and adaptive estimation of the principal subspace in the high dimensional setting.
Cai, T. Tony, Ma, Zongming, Wu, Yihong
core +7 more sources
Sparse PCA with Oracle Property. [PDF]
Adv Neural Inf Process Syst, 2014 In this paper, we study the estimation of the $k$-dimensional sparse principal subspace of covariance matrix $Σ$ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori.
Gu Q, Wang Z, Liu H.
europepmc +5 more sources
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
openaire +4 more sources
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
doaj +1 more source
The Complexity of Sparse Tensor PCA
CoRR, 2021 ISBN ...
Choo, Davin, D'Orsi, Tommaso
openaire +4 more sources
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
openaire +3 more sources
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
openaire +2 more sources
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
doaj +1 more source