Outcome labeling ambiguity and subjectivity are ubiquitous in real-world...
The information bottleneck (IB) principle has been adopted to explain de...
Recent studies revealed the mathematical connection of deep neural netwo...
Low-rank approximation models of data matrices have become important mac...
Non-adversarial generative models such as variational auto-encoder (VAE)...
Matrix decomposition is one of the fundamental tools to discover knowled...
Convolutional neural network (CNN) and its variants have led to many
sta...
Principal component analysis (PCA) is one of the most widely used dimens...
Sparse Singular Value Decomposition (SVD) models have been proposed for
...
Matrix decomposition is a popular and fundamental approach in machine
le...
Given two data matrices X and Y, sparse canonical correlation analysis
(...
Nonnegative matrix factorization is a powerful technique to realize dime...
Nonnegative matrix factorization (NMF) is a powerful tool in data explor...
Molecular profiling data (e.g., gene expression) has been used for clini...
Learning the "blocking" structure is a central challenge for high dimens...