Changshui Zhang

TL;DR: This paper proposes a semi-supervised dimensionality reduction framework, which can efficiently handle the unlabeled data and can significantly improve the accuracy rates of the corresponding supervised and unsupervised approaches.

TL;DR: A novel graph-based transductive classification approach, called linear neighborhood propagation, is proposed, which provides a novel graph structure construction method by introducing multiple-wise edges instead of pairwise edges, and presents an effective scheme to estimate the weights for such multiple- Wise edges.

TL;DR: This paper proposes an iterative greedy search strategy to estimate F0s one by one, to avoid the combinatorial problem of concurrent F0 estimation, and proposes a polyphony estimation method to terminate the iterative process.

TL;DR: This paper proposes an image captioning system that exploits the parallel structures between images and sentences and makes another novel modeling contribution by introducing scene-specific contexts that capture higher-level semantic information encoded in an image.

TL;DR: This paper proposes a novel semi-supervised orthogonal discriminant analysis via label propagation that propagates the label information from the labeled data to the unlabeled data through a specially designed label propagation, and thus the distribution of the unl labeled data can be explored more effectively to learn a better subspace.