Conference proceeding
IMMC: incremental maximum margin criterion
Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp.725-730
KDD '04
08/22/2004
DOI: 10.1145/1014052.1014147
Abstract
Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or large-scale data. To meet this desirability, Incremental Principal Component Analysis (IPCA) algorithm has been well established, but it is an unsupervised subspace learning approach and is not optimal for general classification tasks, such as face recognition and Web document categorization. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Maximum Margin Criterion (IMMC), to infer an adaptive subspace by optimizing the Maximum Margin Criterion. We also present the proof for convergence of the proposed algorithm. Experimental results on both synthetic dataset and real world datasets show that IMMC converges to the similar subspace as that of batch approach.
Details
- Title: Subtitle
- IMMC: incremental maximum margin criterion
- Creators
- Jun Yan - Peking UniversityBenyu Zhang - Microsoft Research AsiaShuicheng Yan - Peking UniversityQiang Yang - Hong Kong University of Science and TechnologyHua Li - Peking UniversityZheng Chen - Microsoft Research AsiaWensi Xi - Virginia TechWeiguo Fan - Virginia TechWei-Ying Ma - Microsoft Research AsiaQiansheng Cheng - Peking University
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp.725-730
- Publisher
- ACM
- Series
- KDD '04
- DOI
- 10.1145/1014052.1014147
- Language
- English
- Date published
- 08/22/2004
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380484502771
Metrics
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