A popular approach for dimensionality reduction and data analysis is principal component analysis (PCA). A limiting factor with PCA is that it does not inform us on which of the o...
Mahdokht Masaeli, Yan Yan, Ying Cui, Glenn Fung, J...
We study sparse principal components analysis in the high-dimensional setting, where p (the number of variables) can be much larger than n (the number of observations). We prove o...
Eigenvalue problems are rampant in machine learning and statistics and appear in the context of classification, dimensionality reduction, etc. In this paper, we consider a cardina...
Bharath K. Sriperumbudur, David A. Torres, Gert R....
—Network delay is a crucial metric for evaluating the state of the network. We present in this paper a structural analysis of network delay, based on delay measurements of a back...
—This paper presents a fast part-based subspace selection algorithm, termed the binary sparse nonnegative matrix factorization (B-SNMF). Both the training process and the testing...