Adjustment of systematic microarray data biases
Bioinformatics
Generalized Power Method for Sparse Principal Component Analysis
The Journal of Machine Learning Research
Sparse unsupervised dimensionality reduction algorithms
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Improve robustness of sparse PCA by L1-norm maximization
Pattern Recognition
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Discovering the transcriptional modules using microarray data by penalized matrix decomposition
Computers in Biology and Medicine
Sparse PCA by iterative elimination algorithm
Advances in Computational Mathematics
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Feature selection from high-order tensorial data via sparse decomposition
Pattern Recognition Letters
Sparse principal component analysis by choice of norm
Journal of Multivariate Analysis
Consistency of sparse PCA in High Dimension, Low Sample Size contexts
Journal of Multivariate Analysis
A coordinate descent MM algorithm for fast computation of sparse logistic PCA
Computational Statistics & Data Analysis
Truncated power method for sparse eigenvalue problems
The Journal of Machine Learning Research
A robust elastic net approach for feature learning
Journal of Visual Communication and Image Representation
Subspace clustering of high-dimensional data: a predictive approach
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Principal component analysis (PCA) is a widely used tool for data analysis and dimension reduction in applications throughout science and engineering. However, the principal components (PCs) can sometimes be difficult to interpret, because they are linear combinations of all the original variables. To facilitate interpretation, sparse PCA produces modified PCs with sparse loadings, i.e. loadings with very few non-zero elements. In this paper, we propose a new sparse PCA method, namely sparse PCA via regularized SVD (sPCA-rSVD). We use the connection of PCA with singular value decomposition (SVD) of the data matrix and extract the PCs through solving a low rank matrix approximation problem. Regularization penalties are introduced to the corresponding minimization problem to promote sparsity in PC loadings. An efficient iterative algorithm is proposed for computation. Two tuning parameter selection methods are discussed. Some theoretical results are established to justify the use of sPCA-rSVD when only the data covariance matrix is available. In addition, we give a modified definition of variance explained by the sparse PCs. The sPCA-rSVD provides a uniform treatment of both classical multivariate data and high-dimension-low-sample-size (HDLSS) data. Further understanding of sPCA-rSVD and some existing alternatives is gained through simulation studies and real data examples, which suggests that sPCA-rSVD provides competitive results.