We present a fast non-iterative method for approximating the leading eigenvector so as to render graph-spectral based grouping algorithms more efficient. The approximation is base...
Antonio Robles-Kelly, Sudeep Sarkar, Edwin R. Hanc...
Abstract. Matrix factorization is a fundamental building block in many computer vision and machine learning algorithms. In this work we focus on the problem of ”structure from mo...
Non-negative matrix factorization (NMF) provides a lower rank approximation of a matrix. Due to nonnegativity imposed on the factors, it gives a latent structure that is often mor...
Abstract. When studying the ε-pseudospectrum of a matrix, one is often interested in computing the extremal points having maximum real part or modulus. This is a crucial step, for...
Abstract-- In peer-to-peer (P2P) networks, reputation aggregation and peer ranking are the most time-consuming and spacedemanding operations. This paper proposes a gossip-based rep...