Now showing items 1-3 of 3
Limited Memory Block Krylov Subspace Optimization for Computing Dominant Singular Value Decompositions
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 ...
An Efficient Gauss-Newton Algorithm for Symmetric Low-Rank Product Matrix Approximatins
We derive and study a Gauss-Newton method for computing the symmetric low-rank product (SLRP) XXT, where X / Rnkfor k<n, that is the closest approximation to a given symmetric matrix A / Rnn in Frobenius norm. When A=BTB ...
Trace-Penalty Minimization for Large-scale Eigenspace Computation
The Rayleigh-Ritz (RR) procedure, including orthogonalization, constitutes a major bottleneck in computing relatively high-dimensional eigenspaces of large sparse matrices. Although operations involved in RR steps can be ...