Year: 2011
Type: preprint
Abstract: This paper considers the sparse eigenvalue problem, which is to extract dominant (largest) sparse eigenvectors with at most $k$ non-zero components. We propose a simple yet effective solution called truncated power method that can approximately solve the underlying nonconvex optimization problem. A strong sparse recovery result is proved for the truncated power method, and this theory is our key motivation for developing the new algorithm. The proposed method is tested on applications such as sparse principal component analysis and the densest $k$-subgraph problem. Extensive experiments on several synthetic and real-world large scale datasets demonstrate the competitive empirical performance of our method.
Source: arXiv (Cornell University)
Authors: Yuan, Xiao-Tong, Zhang, Tong
Institution: Rutgers, The State University of New Jersey
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2137404238