Decomposition-based tensor learning regression for improved classification of multimedia

Existing vector-based multimedia classification often incurs loss of space-time information and requires generation of high-dimensional vectors. To explore a possible new solution for the problem, we propose a novel tensor-based logistic regression algorithm via Tucker decomposition to complete multimedia classification. In order to strengthen the classification process, ℓF-norm is used for regularization term. A logistic Tucker regression model is established to achieve effective extraction of principal components out of the tensors, and hence reduce the dimension of inputs to improve the efficiency of multimedia classification. To evaluate the proposed algorithm, we carried out extensive experiments on a number of data sets, including two second-order grayscale image datasets and one third-order video sequence dataset. All the results indicate that our proposed algorithm outperforms the existing state-of-the-arts in relevant areas.