159 citations · 739 across the 19 of their papers we have counts for
7 papers · 1 filter
Large Margin Multi-modal Multi-task Feature Extraction for Image Classification
Yong Luo, Yonggang Wen, Dacheng Tao +2
The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many…
Heterogeneous Multi-task Metric Learning across Multiple Domains
Yong Luo, Yonggang Wen, Dacheng Tao
Distance metric learning (DML) plays a crucial role in diverse machine learning algorithms and applications. When the labeled information in target domain is limited, transfer metr…
Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain
Yong Luo, Yonggang Wen, Tongliang Liu +1
The goal of transfer learning is to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. In this paper, w…
Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification
Yong Luo, Dacheng Tao, Chang Xu +3
In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several obj…
Multi-View Matrix Completion for Multi-Label Image Classification
Yong Luo, Tongliang Liu, Dacheng Tao +1
There is growing interest in multi-label image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and brow…
Transfer Metric Learning: Algorithms, Applications and Outlooks
Yong Luo, Yonggang Wen, Ling-Yu Duan +1
Distance metric learning (DML) aims to find an appropriate way to reveal the underlying data relationship. It is critical in many machine learning, pattern recognition and data min…
Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction
Yong Luo, Dacheng Tao, Yonggang Wen +2
Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications.…