Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

X≈ WH, W∈ RM× R, H∈ RN× R,(1) to 'explain'the data matrix X, where W≥ 0, H≥ 0, and
R≤ min {M, N}. At first glance, NMF is nothing but an alternative factorization model to …

Principal components analysis (PCA) is a well-known technique for approximating a tabular
data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets …

We propose a temporal latent space model for link prediction in dynamic social networks,
where the goal is to predict links over time based on a sequence of previous graph …

Topic models provide a useful method for dimensionality reduction and exploratory data
analysis in large text corpora. Most approaches to topic model learning have been based on …

In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …

The Nonnegative Matrix Factorization (NMF) problem has a rich history spanning quantum
mechanics, probability theory, data analysis, polyhedral combinatorics, communication …

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …

Robust subspace clustering Page 1 The Annals of Statistics 2014, Vol. 42, No. 2, 669–699
DOI: 10.1214/13-AOS1199 © Institute of Mathematical Statistics, 2014 ROBUST …

We introduce and analyze stochastic optimization methods where the input to each update
is perturbed by bounded noise. We show that this framework forms the basis of a unified …