dominant singular value decomposition

A Distributed and Secure Algorithm for Computing Dominant SVD Based on Projection Splitting

Published: 2020/12/06, Updated: 2020/12/18

Constrained Nonlinear Optimization, Parallel Algorithms alternating direction method of multipliers, data security, distributed computing, dominant singular value decomposition

In this paper, we propose and study a distributed and secure algorithm for computing dominant (or truncated) singular value decompositions (SVD) of large and distributed data matrices. We consider the scenario where each node privately holds a subset of columns and only exchanges “safe” information with other nodes in a collaborative effort to calculate a … Read more

Limited Memory Block Krylov Subspace Optimization for Computing Dominant Singular Value Decompositions

Published: 2012/03/22, Updated: 2012/03/23

Xin Liu

Zaiwen Wen

Yin Zhang

Constrained Nonlinear Optimization, Data-Mining dominant singular value decomposition, eigenvalue decomposition, krylov subspace, subspace optimization

In many data-intensive applications, the use of principal component analysis (PCA) and other related techniques is ubiquitous for dimension reduction, data mining or other transformational purposes. Such transformations often require efficiently, reliably and accurately computing dominant singular value decompositions (SVDs) of large unstructured matrices. In this paper, we propose and study a subspace optimization technique … Read more