document: 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 .
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