5 papers
A New Regression Lens on Multi-Class Classification
Xin Bing, Bingqing Li, Marten Wegkamp
Linear Discriminant Analysis (LDA) is a fundamental method for classification. Its simple linear structure facilitates interpretation, and it is naturally suited to multi-class set…
High-Dimensional Invariant Tests of Multivariate Normality Based on Radial Concentration
Xin Bing, Derek Latremouille
While the problem of testing multivariate normality has received considerable attention in the classical low-dimensional setting where the sample size is much larger than the f…
Convergence and Optimality of the EM Algorithm Under Multi-Component Gaussian Mixture Models
Xin Bing, Dehan Kong, Bingqing Li
Gaussian mixture models (GMMs) are fundamental statistical tools for modeling heterogeneous data. Due to the nonconcavity of the likelihood function, the Expectation-Maximization (…
Learning large softmax mixtures with warm start EM
Xin Bing, Florentina Bunea, Jonathan Niles-Weed +1
Softmax mixture models (SMMs) are discrete -mixtures introduced to model the probability of choosing an attribute $x_j \in \RR^L$ from candidates, in heterogeneous populatio…
Optimal Discriminant Analysis in High-Dimensional Latent Factor Models
Xin Bing, Marten Wegkamp
In high-dimensional classification problems, a commonly used approach is to first project the high-dimensional features into a lower dimensional space, and base the classification…