To tackle this problem, we propose manifold learning with structured subspace for multi-label feature selection. Specifically, we first uncover a latent subspace for a more compact and accurate data representat
Existing multi-label feature selection methods commonly employ graph regularization to formulate sparse regression objectives based on manifold learning. However, these approaches face two primary challenges. First, they typically rely on fixed similarities derived from the original feature space, which can...
Multi-label learning As more and more high-throughput proteome data are collected, automated annotation of protein function has been one of the most challenging problems of the post-genomic era. To address this challenge, we propose a novel functional annotation framework incorporating manifold ...
2. Person re-identification based on multi-instance multi-label learning [J] . Lin Ying, Guo Feng, Cao Liujuan, Neurocomputing . 2016,第DECa12期 机译:基于多实例多标签学习的人员重新识别 3. Regularizing extreme learning machine by dual locally linear embedding manifold learning for training...
Zhou, B., Lapedriza, À., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: NIPS, pp. 487–495 (2014) Google Scholar Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from...
Belkin, et al., “Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples”, retrieved on Oct. 23, 2008 at <>, Journal of Machine Learning Research, vol. 7, 2006, pp. 2399-2434. Bennett, et al., “Semi-Supervised Support Vector Machines”, retrieve...
It is well-known that exploiting label correlations is important to multi-label learning. Existing approaches either assume that the label correlations are global and shared by all instances; or that the label correlations are local and shared only by a data subset. In fact, in the real-worl...
Multi-layer neural networks excel at simulating such nonlinear manifold structures without relying on hypotheses. They can acquire high-level data features by learning the hierarchical structure. Therefore, this paper utilizes a neural network to efficiently learn the multi-label data with increased ...
[28]. In the manifold learning paradigm, sparse coefficients for MLFS were derived by using an objective function that integrates manifold regularization and dependence maximization [29]. Furthermore, the correlation between labels was assessed through iterative optimization to refine the label ...
He is now an associate professor at the Zhijiang College, Zhejiang University of Technology. His research interests include pattern recognition, intelligence computation and manifold learning. He has published over 20 refereed papers. Yuan-Hai Shao received his B.S. degree in the College of ...