Incremental minmax projection analysis for classification
For data classification, the standard implementation of projection algorithms do not scale well with large dataset size. It makes the computation of large samples infeasible. In this paper, we utilize a block optimization strategy to propose a new locally discriminant projection algorithm termed minmax projection analysis (MMPA). The algorithm takes into account both intra-class and interclass geometries and also possesses the orthogonality property. Furthermore, an incremental MMPA is proposed to learn the local discriminant subspace with newly inserted data by employing the idea of singular value decomposition updating algorithm. Moreover, we extend MMPA to the semi-supervised case and nonlinear case, namely, semi-supervised MMPA and kernel MMPA. The experimental results on image database, hand written digit database, and face database demonstrate the effectiveness of those proposed algorithms.
singular value decomposition dimensionality reduction incremental learning orthogonal locally discriminant