Tensor classification network

Abstract

The growing interests in multi-way data analysis have made the tensor factorization and classification a crucial issue in machine learning for signal processing. Conventional neural network (NN) classifier is estimated from a set of input vectors. The multi-way data are unfolded as high-dimensional vectors for model training. The classification performance is constrained because the neighboring temporal or spatial information in different ways is lost in the trained NN classifier. More parameters are required to learn the complicated data structure. This paper presents a new tensor classification network (TCN) which combines the tensor factorization and the NN classification for multi-way feature extraction and classification. We generalize from NN classifier to TCN where the multi-way information is preserved through projecting the input tensors into latent tensors. The projection over tensor space is efficiently characterized so that a very compact classifier can be achieved. Experimental results on image recognition demonstrate that TCN attains comparable classification performance but with very few parameters (as small as 6.7%) compared with NN classifier.