We propose an algorithm for recovering the matrix A in X = AS where X is a random vector of lower dimension than S. S is assumed to be sparse in the sense that S has less nonzero e...
Fabian J. Theis, Pando G. Georgiev, Andrzej Cichoc...
Given a large sparse graph, how can we find patterns and anomalies? Several important applications can be modeled as large sparse graphs, e.g., network traffic monitoring, resea...
In the last few years, we have witnessed an explosion in applications of sparse representation, the majority of which share the need for finding sparse solutions of underdetermine...
Armin Eftekhari, Massoud Babaie-Zadeh, Christian J...
Abstract. We improve the performance of sparse matrix-vector multiplication (SpMV) on modern cache-based superscalar machines when the matrix structure consists of multiple, irregu...
Recent study has shown that canonical algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) can be obtained from graph based dimensionality ...