Multiple feature selection and fusion based on generalized N-dimensional independent component analysis

This paper proposes a framework of tensor-based ICA method for N-dimensional data analysis, which is called generalized N-dimensional ICA (GND-ICA). The proposed GND-ICA is based on multilinear algebra that treats N-dimensional data as a tensor without any unfolding preprocess. As an application, the GND-ICA can be used for multiple feature fusion and representation for color image classification. Multiple features extracted from a given image are constructed as a tensor. The effective components for each feature can be selected simultaneously and combined by the GND-ICA. This can obtain the improved classification results in comparison with various conventional linear and multilinear subspace learning methods.