| Paper ID | MLSP-15.6 |
| Paper Title |
FAST LOCAL REPRESENTATION LEARNING WITH ADAPTIVE ANCHOR GRAPH |
| Authors |
Canyu Zhang, Feiping Nie, Zheng Wang, School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, China; Rong Wang, School of Computer Science, Center for OPTical IMagery Analysis and Learning (OPTIMAL) and School of Cybersecurity, Northwestern Polytechnical University, China; Xuelong Li, School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, China |
| Session | MLSP-15: Learning Algorithms 2 |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation Time: | Wednesday, 09 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-SBML] Subspace and manifold learning |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Dimension reduction is an effective technology to embed data with high dimension to lower subspace, where Linear Discriminant Analysis (LDA), one of representative methods, only works with Gaussian distribution data. However, in order to solve non-Gaussian issue that only one cluster cannot well fit the distribution of same class, many graph-based discriminant analysis methods are proposed which capture local structure through measuring each pairwise distance. This is expense of time complexity because of the full-connections. In order to solve this issue, we propose a fast local representation learning with adaptive anchor graph to learn local structure information through similarity matrix in anchor-based graph. Notably, anchor points and similarity matrix are updated in subspace which is more precisely to capture local discriminant information. Experimental results on several synthetic and well-known datasets demonstrate the advantages of our method over the state-of-the-art methods. |
