Rather than traditional pattern and signal analysis problems, label distribution learning aims
to predicate the probabilistic labels of appended instances. In this work, a novel approach to …

As an important structure of data handling, many categories of network data are normally
organized as graphs, and graph routes represent the intrinsic connectivity of vertices, which …

Online Nonnegative Matrix Factorization aims to learn the nonnegative factorizations of
sequential data with temporal learning. Existing methods analyze each instance separately …