Fuzzy Bilinear Latent Canonical Correlation Projection For Feature Learning

Canonical correlation analysis (CCA) is a widely used linear unsupervised subspace learning method. However, standard CCA works with vectorized representation of image matrix, which loses the spatial structure information of image data. In addition, a real-world observation often simultaneously belongs to multiple distinct classes with different degrees of membership, while conventional CCA methods can not deal with this situation. Inspired by aforementioned issues, we in this paper propose a fuzzy bilinear canonical correlation projection (FBCCP) approach. FBCCP not only considers two-dimensional spatial structure of images, but also membership degree of practical observation belonging to different classes at the same time. Experimental results on visual recognition show that the proposed FBCCP approach is more effective than related feature learning approaches.