msPCA: An R Package for Sparse PCA with Multiple Components

• URL: http://arxiv.org/abs/2607.05229v1
• Date: Mon, 06 Jul 2026 15:42:00 GMT
• Title: msPCA: An R Package for Sparse PCA with Multiple Components
• Abstract summary: msPCA is an open-source package for sparse principal component analysis with multiple components.<n>It generates a set of sparse loading vectors that collectively explain a large fraction of variance in a dataset, while remaining non-redundant.
• Score: 2.0052993723676895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present msPCA: an open-source R package for sparse principal component analysis with multiple components. It implements an alternating maximization algorithm to generate a set of sparse loading vectors that collectively explain a large fraction of the variance in a dataset, while remaining non-redundant. The algorithm supports two definitions of non-redundancy: either orthogonality of the loading vectors or zero pairwise correlation between principal components (PCs). In the reported benchmarks, msPCA solves sparse PCA problems with thousands of features, achieving competitive runtimes while producing sparse components with controlled feasibility violations and a high fraction of variance explained.

Related papers

• Shape Operator PCA: Curvature-Aware Projections for Geometric Machine Learning [52.452902154360565]
  SHOPCA (Shape Operator-based Principal Component Analysis) is a novel method for unsupervised metric learning and dimensionality reduction.<n>We evaluate SHOPCA on more than 50 real-world benchmark datasets.
  arXiv  Detail & Related papers  (2026-08-15T16:37:47Z)
• Sparse PCA with False Discovery Rate Controlled Variable Selection [12.167049432063129]
  We propose an alternative formulation of sparse PCA driven by the false discovery rate (FDR) A major advantage of the resulting T-Rex PCA is that no sparsity parameter tuning is required. Numerical experiments and a stock market data example demonstrate a significant performance improvement.
  arXiv  Detail & Related papers  (2024-01-16T14:07:36Z)
• Nearly-Linear Time and Streaming Algorithms for Outlier-Robust PCA [43.106438224356175]
  We develop a nearly-linear time algorithm for robust PCA with near-optimal error guarantees. We also develop a single-pass streaming algorithm for robust PCA with memory usage nearly-linear in the dimension.
  arXiv  Detail & Related papers  (2023-05-04T04:45:16Z)
• Sparse PCA With Multiple Components [2.1485350418225244]
  Sparse Principal Component Analysis (sPCA) is a technique for obtaining combinations of features that explain variance of high-dimensional datasets in an interpretable manner.<n>Most existing PCA methods do not guarantee the optimality, let alone the optimality, of the resulting solution when we seek multiple sparse PCs.<n>We propose exact methods and rounding mechanisms that, together, obtain solutions with a bound gap on the order of 0%-15% for real-world datasets.
  arXiv  Detail & Related papers  (2022-09-29T13:57:18Z)
• Sparse PCA on fixed-rank matrices [0.05076419064097732]
  We show that, if the rank of the covariance matrix is a fixed value, then there is an algorithm that solves sparse PCA to global optimality. We also prove a similar result for the version of sparse PCA which requires the principal components to have disjoint supports.
  arXiv  Detail & Related papers  (2022-01-07T15:05:32Z)
• CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator [60.799183326613395]
  We propose an unbiased estimator for categorical random variables based on multiple mutually negatively correlated (jointly antithetic) samples. CARMS combines REINFORCE with copula based sampling to avoid duplicate samples and reduce its variance, while keeping the estimator unbiased using importance sampling. We evaluate CARMS on several benchmark datasets on a generative modeling task, as well as a structured output prediction task, and find it to outperform competing methods including a strong self-control baseline.
  arXiv  Detail & Related papers  (2021-10-26T20:14:30Z)
• Shared Independent Component Analysis for Multi-Subject Neuroimaging [107.29179765643042]
  We introduce Shared Independent Component Analysis (ShICA) that models each view as a linear transform of shared independent components contaminated by additive Gaussian noise. We show that this model is identifiable if the components are either non-Gaussian or have enough diversity in noise variances. We provide empirical evidence on fMRI and MEG datasets that ShICA yields more accurate estimation of the components than alternatives.
  arXiv  Detail & Related papers  (2021-10-26T08:54:41Z)
• Examining and Combating Spurious Features under Distribution Shift [94.31956965507085]
  We define and analyze robust and spurious representations using the information-theoretic concept of minimal sufficient statistics. We prove that even when there is only bias of the input distribution, models can still pick up spurious features from their training data. Inspired by our analysis, we demonstrate that group DRO can fail when groups do not directly account for various spurious correlations.
  arXiv  Detail & Related papers  (2021-06-14T05:39:09Z)
• Enhanced Principal Component Analysis under A Collaborative-Robust Framework [89.28334359066258]
  We introduce a general collaborative-robust weight learning framework that combines weight learning and robust loss in a non-trivial way. Under the proposed framework, only a part of well-fitting samples are activated which indicates more importance during training, and others, whose errors are large, will not be ignored. In particular, the negative effects of inactivated samples are alleviated by the robust loss function.
  arXiv  Detail & Related papers  (2021-03-22T15:17:37Z)
• A New Basis for Sparse Principal Component Analysis [5.258928416164104]
  Previous versions of sparse principal component analysis presumed that the eigen-basis (a $p times k$ matrix) is approximately sparse. We propose a method that presumes the $p times k$ matrix becomes approximately sparse after a $k times k$ rotation. We show that for the same level of sparsity, the proposed sparse PCA method is more stable and can explain more variance compared to alternative methods.
  arXiv  Detail & Related papers  (2020-07-01T16:32:22Z)
• Approximation Algorithms for Sparse Principal Component Analysis [57.5357874512594]
  Principal component analysis (PCA) is a widely used dimension reduction technique in machine learning and statistics. Various approaches to obtain sparse principal direction loadings have been proposed, which are termed Sparse Principal Component Analysis. We present thresholding as a provably accurate, time, approximation algorithm for the SPCA problem.
  arXiv  Detail & Related papers  (2020-06-23T04:25:36Z)
• Robustly Learning any Clusterable Mixture of Gaussians [55.41573600814391]
  We study the efficient learnability of high-dimensional Gaussian mixtures in the adversarial-robust setting. We provide an algorithm that learns the components of an $epsilon$-corrupted $k$-mixture within information theoretically near-optimal error proofs of $tildeO(epsilon)$. Our main technical contribution is a new robust identifiability proof clusters from a Gaussian mixture, which can be captured by the constant-degree Sum of Squares proof system.
  arXiv  Detail & Related papers  (2020-05-13T16:44:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.