Paper Info
Title
Computational Advances In Sparse L1-Norm Principal-Component Analysis Of Multi-Dimensional Data
Abstract
We consider the problem of extracting a sparse L-1-norm principal component from a data matrix X is an element of R-DxN of N observation vectors of dimension D. Recently, an optimal algorithm was presented in the literature for the computation of sparse L-1-norm principal components with complexity O(N-S) where S is the desired sparsity. In this paper, we present an efficient suboptimal algorithm of complexity O (N-2 (N + D)). Extensive numerical studies demonstrate the near-optimal performance of the proposed algorithm and its strong resistance to faulty measurements/outliers in the data matrix.
Year
Venue
Keywords
2017
2017 IEEE 7TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP)
Eigen-vectors, faulty data, feature extraction, L-1-norm, L-2-norm, outlier resistance, sparse principal-component analysis
Field
DocType
Citations 
Convergence (routing),Multi dimensional data,Computer science,Outlier,Algorithm,Principal component analysis,Sparse matrix,Computation
Conference
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Shubham Chamadia133.78
Dimitris Pados220826.49