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...
We consider the minimization of a smooth loss with trace-norm regularization, which is a natural objective in multi-class and multitask learning. Even though the problem is convex...
Recently, a number of researchers have proposed spectral algorithms for learning models of dynamical systems—for example, Hidden Markov Models (HMMs), Partially Observable Marko...
Traditional recommendation algorithms often select products with the highest predicted ratings to recommend. However, earlier research in economics and marketing indicates that a ...
Higher-order tensors are used in many application fields, such as statistics, signal processing, and scientific computing. Efficient and reliable algorithms for manipulating thes...
Mariya Ishteva, Pierre-Antoine Absil, Sabine Van H...
In this paper we present a fast and accurate procedure called clustered low rank matrix approximation for massive graphs. The procedure involves a fast clustering of the graph and...
A binary matrix is fully nested if its columns form a chain of subsets; that is, any two columns are ordered by the subset relation, where we view each column as a subset of the r...
We develop a hierarchical matrix construction algorithm using matrixvector multiplications, based on the randomized singular value decomposition of low-rank matrices. The algorith...
: We review the singular value decomposition (SVD) and discuss some lesser-known applications of it that we find particularly interesting. We also discuss generalizations of the S...
With the technical advances in ubiquitous computing and wireless networking, there has been an increasing need to capture the context information (such as the location) and to figu...
Hyuk Lim, Lu-Chuan Kung, Jennifer C. Hou, Haiyun L...