We propose a symmetric low-rank representation (SLRR) method for subspace clustering, which assumes that a data set is approximately drawn from the union of multiple subspaces. The proposed technique can reveal the membership of multiple subspaces through the self-expressiveness property of the data. In particular, the SLRR method considers a collaborative representation combined with low-rank ...

In this paper, we introduce an ensemble speaker modeling using a speaker adaptive training (SAT) deep neural network (SAT-DNN). We first train a speaker-independent DNN (SIDNN) acoustic model as a universal speaker model (USM). Based on the USM, a SAT-DNN is used to obtain a set of speaker-dependent models by assuming that all other layers except one speaker-dependent (SD) layer are shared amon...

In this article, we focus on solving a sequence of linear systems that have identical (or similar) coefficient matrices. For type problem, investigate subspace correction (SC) and deflation methods, which use an auxiliary matrix (subspace) to accelerate the convergence iterative method. practical simulations, these acceleration methods typically work well when range contains eigenspaces corresp...

Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-revealing QR decomposition, play a central role in data analysis and scientific computing. This work surveys recent research which demonstrates that randomization offers a powerful tool for performing low-rank matrix approximation. These techniques exploit modern computational architectures more full...

Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-revealing QR decomposition, play a central role in data analysis and scientific computing. This work surveys and extends recent research which demonstrates that randomization offers a powerful tool for performing low-rank matrix approximation. These techniques exploit modern computational architectur...

In this paper we show how to compute recursively an approximation of the left and right dominant singular subspaces of a given matrix. In order to perform as few as possible operations on each column of the matrix, we use a variant of the classical Gram–Schmidt algorithm to estimate this subspace. The method is shown to be particularly suited for matrices with many more rows than columns. Bound...

Computational time reversal imaging can be used to locate the position of multiple scatterers in a known background medium. The current methods for computational time reversal imaging are based on the null subspace projection operator, obtained through the singular value decomposition of the frequency response matrix. Here, we discuss the image recovery problem from a small number of random and...

Spectrum sensing has been put forward to make more efficient use of scarce radio frequency spectrum. The leading eigenvector of the sample covariance matrix has been applied to spectrum sensing under the frameworks of PCA and kernel PCA. In this paper, spectrum sensing with subspace matching is proposed. The subspace is comprised of the eigenvectors corresponding to dominant non-zero eigenvalue...