Heteroscedastic discriminant analysis with two-dimensional constraints

Sibao Chen, Yu Hu, Bin Luo, Ren-Hua Wang

Abstract: Heteroscedastic discriminant analysis (HDA) with two-dimensional (2D) constraints is proposed in this paper. HDA suffers from the small sample size problem and instability when lack of training data or feature dimension is high, even when the number of dimension is in a suitable range. Two-dimensional HDA is first proposed, then we show that 2D methods are actually a kind of structure-constrained 1D methods, and lastly, HDA with 2D constraints is proposed. Experiments on TIMIT and WSJ0 show that the proposed method outperforms other methods.