Based on linear regression, a novel method called reconstructive discriminant analysis (RDA) is developed for feature extraction and dimensionality reduction (DR). RDA is induced from linear Regression classification (LRC). LRC assumes each class lies on a linear subspace and finds the nearest subspace for a given sample. But the original space cannot guarantee that the given sample matches its...

Recently, low-rank and sparse model-based dimensionality reduction (DR) methods have aroused lots of interest. In this paper, we propose an effective supervised DR technique named block-diagonal constrained low-rank and sparse-based embedding (BLSE). BLSE has two steps, i.e., block-diagonal constrained low-rank and sparse representation (BLSR) and block-diagonal constrained low-rank and sparse ...

We present an algorithm for modelling flow cytometry data in the presence of large inter-sample variation. Large-scale cytometry datasets often exhibit some within-class variation due to technical effects such as instrumental differences and variations in data acquisition, as well as subtle biological heterogeneity within the class of samples. Failure to account for such variations in the model...

Abstract Facial expression recognition is a fine-grained task because different emotions have subtle facial movements. This paper proposes to learn inter-class optical flow difference using generative adversarial networks (GANs) for recognition. Initially, the proposed method employs GAN produce images from between static fully expressive samples and neutral samples. Such used highlight displac...