Abstract:
With both theoretical and practical significance, the study of sparse binary projection models has attracted considerable recent research attention. The models project dense input samples into a higher-dimensional space and output sparse binary data representations after the Winner-Take-All competition, subject to the constraint that the projection matrix is also sparse and binary. Following the work along this line, we developed a supervised-WTA model which works when training samples with both input and output representations are available, from which the optimal projection matrix can be obtained with a simple, efficient, yet effective algorithm after proper relaxation. We further extended the model and the algorithm to an unsupervised setting where only the input representation of the samples is available. In a series of empirical evaluation on similarity search tasks, the proposed models reported significantly improved results over state-of-the-art methods in both search accuracies and running speed. The successful results give us strong confidence that the work provides a useful tool for industrial applications.
