Abstract Sparse Group Lasso is a method of linear regression analysis that finds sparse
parameters in terms of both feature groups and individual features. Block Coordinate …

Adaptive learning rate algorithms such as RMSProp are widely used for training deep neural
networks. RMSProp offers efficient training since it uses first order gradients to approximate …

Since manually labeling images is expensive and labor intensive, in practice we often do not
have enough labeled images to train an effective classifier for the new image classification …

Regularized discrete optimal transport (OT) is a powerful tool to measure the distance
between two discrete distributions that have been constructed from data samples on two …

Obtaining labeled images and samples is a very expensive process and can require
intensive labor. At the same time, there are often not enough labeled samples to train an …

K-Multiple-Means is an extension of K-means for the clustering of multiple means used in
many applications, such as image segmentation, load balancing, and blind-source …

Radio map reconstruction is a fundamental problem of great relevance in numerous real‐
world applications, such as network planning and fingerprint localization. Sampling the …

Anchor graph hashing is used in many applications such as cancer detection, web page
classification, and drug discovery. It computes the hash codes from the eigenvectors of the …

Non-convex sparse regularizations with group structures are useful tools for selecting
important feature groups. For optimization with these regularizations, block coordinate …

On many occasions, the evaluation of a phenomenon based on a single feature could not
solely be resulted in comprehensive and accurate results. Moreover, even if we have …