223 citations · 268 across the 5 of their papers we have counts for
5 papers · 1 filter
Discovering Support and Affiliated Features from Very High Dimensions
Yiteng Zhai, Mingkui Tan, Ivor Tsang +1
In this paper, a novel learning paradigm is presented to automatically identify groups of informative and correlated features from very high dimensions. Specifically, we explicitly…
Learning with Augmented Features for Heterogeneous Domain Adaptation
Lixin Duan, Dong Xu, Ivor Tsang
We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features…
A Split-Merge Framework for Comparing Clusterings
Qiaoliang Xiang, Qi Mao, Kian Ming Chai +3
Clustering evaluation measures are frequently used to evaluate the performance of algorithms. However, most measures are not properly normalized and ignore some information in the…
Parameter-Free Spectral Kernel Learning
Qi Mao, Ivor W. Tsang
Due to the growing ubiquity of unlabeled data, learning with unlabeled data is attracting increasing attention in machine learning. In this paper, we propose a novel semi-supervise…
Hierarchical Maximum Margin Learning for Multi-Class Classification
Jian-Bo Yang, Ivor W. Tsang
Due to myriads of classes, designing accurate and efficient classifiers becomes very challenging for multi-class classification. Recent research has shown that class structure lear…