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Likelihood-Free Overcomplete ICA and Applications in Causal Discovery
Chenwei Ding, Mingming Gong, Kun Zhang +1
Causal discovery witnessed significant progress over the past decades. In particular, many recent causal discovery methods make use of independent, non-Gaussian noise to achieve id…
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…