Results 11 to 20 of about 513,547 (285)
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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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.
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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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Sparse PCA Beyond Covariance Thresholding
CoRR, 2023 In the Wishart model for sparse PCA we are given $n$ samples $Y_1,\ldots, Y_n$ drawn independently from a $d$-dimensional Gaussian distribution $N({0, Id + βvv^\top})$, where $β> 0$ and $v\in \mathbb{R}^d$ is a $k$-sparse unit vector, and we wish to recover $v$ (up to sign).
Novikov, Gleb
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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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A Randomized Rounding Algorithm for Sparse PCA [PDF]
ACM Transactions on Knowledge Discovery from Data, 2017 We present and analyze a simple, two-step algorithm to approximate the optimal solution of the sparse PCA problem. In the proposed approach, we first solve an ℓ 1 -penalized version of the NP-hard sparse PCA optimization problem and then we use a randomized rounding strategy to sparsify the resulting dense solution.
Kimon Fountoulakis +3 more
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, 2007 We describe a nonnegative variant of the ”Sparse PCA ” problem. The goal is to create a low dimensional representation from a collection of points which on the one hand maximizes the variance of the projected points and on the other uses only parts of ...
Ron Zass, Amnon Shashua
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Comparing Classical and Robust Sparse PCA
, 2013 The main drawback of principal component analysis (PCA) especially for applications in high dimensions is that the extracted components are linear combinations of all input variables. To facilitate the interpretability of PCA various sparse methods have been proposed recently.
Valentin Todorov, Peter Filzmoser
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Scale-Invariant Sparse PCA on High Dimensional Meta-elliptical Data. [PDF]
J Am Stat Assoc, 2014 We propose a semiparametric method for conducting scale-invariant sparse principal component analysis (PCA) on high-dimensional non-Gaussian data. Compared with sparse PCA, our method has a weaker modeling assumption and is more robust to possible data ...
Han F, Liu H.
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The Complexity of Sparse Tensor PCA
CoRR, 2021 ISBN ...
Choo, Davin, D'Orsi, Tommaso
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