Jun Wang

TL;DR: A novel unified framework that jointly learns feature relationships and exploits the class relationships for improved video classification performance is proposed and demonstrates that the proposed framework exhibits superior performance over several state-of-the-art approaches.

TL;DR: This work proposes a unified alternating optimization framework for multi-GM and defines and uses two metrics related to graphwise and pairwise consistencies and shows two embodiments under the proposed framework that can cope with the nonfactorized and factorized affinity matrix, respectively.

TL;DR: This paper presents a bandit algorithm for conducting online portfolio choices by effectually exploiting correlations among multiple arms and derives the optimal portfolio strategy that represents the combination of passive and active investments according to a risk-adjusted reward function.

TL;DR: In this article, a low-rank linearized SVM (LRLSVM) is proposed to scale up kernel SVM on limited resources, which transforms a nonlinear SVM to a linear one via an approximate empirical kernel map computed from efficient kernel low rank decompositions and theoretically analyzes the gap between the solutions of the approximate and optimal rank- $k$ kernel map, which in turn provides guidance on the sampling scheme of the Nystrom approximation.

TL;DR: This paper synergically leverage the bootstrap aggregating algorithm and the random subspace method into portfolio construction to mitigate estimation error and analyze the behavior and hyperparameter selection of the proposed strategy by simulation, and corroborate its effectiveness by comparing its out-of-sample performance with those of 10 competing strategies on four datasets.