Sparse pca - Open Access .click

Results 21 to 30 of about 90,827 (166)

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
openaire +3 more sources

Incorporating biological information in sparse principal component analysis with application to genomic data

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
doaj +1 more source

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
doaj +1 more source

Penalty-free sparse PCA [PDF]

, 2014
Adachi, Kohei, Trendafilov, Nickolay
core +1 more source

Probabilistic power flow calculation using principal component analysis-based compressive sensing

Frontiers in Energy Research, 2023
The increasing scale of the injection of renewable energy has brought about great uncertainty to the operation of power grid. In this situation, probabilistic power flow (PPF) calculation has been introduced to mitigate the low accuracy of traditional ...
Tonghe Wang +4 more
doaj +1 more source

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.
openaire +3 more sources

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
openaire +3 more sources

Performing Sparse Regularization and Dimension Reduction Simultaneously in Multimodal Data Fusion

Frontiers in Neuroscience, 2019
Collecting multiple modalities of neuroimaging data on the same subject is increasingly becoming the norm in clinical practice and research. Fusing multiple modalities to find related patterns is a challenge in neuroimaging analysis.
Zhengshi Yang +539 more
doaj +1 more source

Sparse logistic principal components analysis for binary data [PDF]

, 2010
We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities ...
Hu, Jianhua, Huang, Jianhua Z., Lee, Seokho +2 more
core +3 more sources

A Survey on Nonconvex Regularization-Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning

IEEE Access, 2018
In the past decade, sparse and low-rank recovery has drawn much attention in many areas such as signal/image processing, statistics, bioinformatics, and machine learning.
Fei Wen, Lei Chu, Peilin Liu, Robert C. Qiu +3 more
doaj +1 more source

principal component analysis
pca
sparse representation

principal component analysis pca
medicine
technology

compressive sensing
sparse principal component analysis