Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
411497 | Neurocomputing | 2016 | 10 Pages |
Reconstructive discriminant analysis (RDA) is an effective dimensionality reduction method that can match well with linear regression classification (LRC). RDA seeks to find projections that can minimize the intra-class reconstruction scatter and simultaneously maximize the inter-class reconstruction scatter of samples. However, RDA needs to select the k heterogeneous nearest subspaces of each sample to construct the inter-class reconstruction scatter and it is very difficult to predefine the parameter k in practical applications. To deal with this problem, we propose a novel method called parameterless reconstructive discriminant analysis (PRDA) in this paper. Compared to traditional RDA, our proposed RDA variant cannot only fit LRC well but also has two important characteristics: (1) the performance of RDA depends on the parameter k that requires manual turning, while ours is parameter-free, and (2) it adaptively estimates the heterogeneous nearest classes for each sample to construct the inter-class reconstruction scatter. To evaluate the performance of the proposed algorithm, we test PRDA and some other state-of-the-art algorithms on some benchmark datasets such as the FERET, AR and ORL face databases. The experimental results demonstrate the effectiveness of our proposed method.