  Inspired by the advances in biological science, 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 when
training samples with both input and output representations are available, from
which the optimal projection matrix can be obtained with a simple, effective
yet efficient algorithm. 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 the
state-of-the-art methods in both search accuracies and running speed. The
successful results give us strong confidence that the work provides a highly
practical tool to real world applications.
