A probabilistic model for Latent Semantic Indexing: Research Articles
Journal of the American Society for Information Science and Technology
Algorithm 844: Computing sparse reduced-rank approximations to sparse matrices
ACM Transactions on Mathematical Software (TOMS)
Optimal Solutions for Sparse Principal Component Analysis
The Journal of Machine Learning Research
Non-negative Sparse Principal Component Analysis for Multidimensional Constrained Optimization
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Fast Algorithms for Approximating the Singular Value Decomposition
ACM Transactions on Knowledge Discovery from Data (TKDD)
Streaming data reduction using low-memory factored representations
Information Sciences: an International Journal
Convex approximations to sparse PCA via Lagrangian duality
Operations Research Letters
Parallel numerical simulation of seismic waves propagation with intel math kernel library
PARA'12 Proceedings of the 11th international conference on Applied Parallel and Scientific Computing
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We consider the problem of computing low-rank approximations of matrices. The novel aspects of our approach are that we require the low-rank approximations to be written in a factorized form with sparse factors, and the degree of sparsity of the factors can be traded off for reduced reconstruction error by certain user-determined parameters. We give a detailed error analysis of our proposed algorithms and compare the computed sparse low-rank approximations with those obtained from singular value decomposition. We present numerical examples arising from some application areas to illustrate the efficiency and accuracy of our algorithms.