IMMC: incremental maximum margin criterion

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. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Jun Yan, Benyu Zhang, Shuicheng Yan, Qiang Yang, Hua Li, Zheng Chen, Wensi Xi, Weiguo Fan, Wei-Ying Ma, QianSheng Cheng
• Keywords: Data Mining, KDD 2004, Subspace Learning Algorithm, Subspace Learning Approach, Subspace Learning Approaches