Abstract--Randomization is a general technique for evaluating the significance of data analysis results. In randomizationbased significance testing, a result is considered to be in...
We present a generative model for performing sparse probabilistic projections, which includes sparse principal component analysis and sparse canonical correlation analysis as spec...
An important problem in many fields is the analysis of counts data to extract meaningful latent components. Methods like Probabilistic Latent Semantic Analysis (PLSA) and Latent ...
Madhusudana V. S. Shashanka, Bhiksha Raj, Paris Sm...
Many applications arising in a variety of fields can be well illustrated by the task of recovering the low-rank and sparse components of a given matrix. Recently, it is discovered...
This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component i...