LAUSR

In this paper, we propose a human-centered image classification via a neural network considering visual and biological features. The proposed method has two novelties. Firstly, we apply Group-Sparse Local Fisher Discriminant Analysis (GS-LFDA) to biological features. GS-LFDA realizes dimensionality reduction and noise elimination for biological features with consideration of local structures and class information. Secondly, we construct a Canonical Correlation Analysis (CCA)-based hidden layer via Discriminative Locality Preserving CCA (DLPCCA). DLPCCA transforms visual features into effective features by considering the relationships with biological information and class information. The CCA-based hidden layer enables transformation of visual features into effective features for image classification from a small number of training samples. Furthermore, once the projection can be obtained in the training phase, elimination of the need for biological data acquisition in the test phase is realized. This is another merit of our method.