Guiguang Ding

TL;DR: JDA aims to jointly adapt both the marginal distribution and conditional distribution in a principled dimensionality reduction procedure, and construct new feature representation that is effective and robust for substantial distribution difference.

TL;DR: This paper aims to reduce the domain difference by jointly matching the features and reweighting the instances across domains in a principled dimensionality reduction procedure, and construct new feature representation that is invariant to both the distribution difference and the irrelevant instances.

TL;DR: A novel transfer learning framework, referred to as Adaptation Regularization based Transfer Learning (ARTL), to model adaptive classifiers in a unified way based on the structural risk minimization principle and the regularization theory, and can significantly outperform state-of-the-art learning methods on several public text and image datasets.

TL;DR: This paper puts forward a novel hashing method, which is referred to Collective Matrix Factorization Hashing (CMFH), which learns unified hash codes by collective matrix factorization with latent factor model from different modalities of one instance, which can not only supports cross-view search but also increases the search accuracy by merging multiple view information sources.

TL;DR: This paper proposes an effective Semantics-Preserving Hashing method, termed SePH, which transforms semantic affinities of training data as supervised information into a probability distribution and approximates it with to-be-learnt hash codes in Hamming space via minimizing the Kullback-Leibler divergence.