Existing multi-view feature extraction methods are based on restrictive assumptions on the connections between feature vectors and input data. These assumptions damage the quality of learned features, and also require more effort on choosing right dimensions of feature vector components connected to each view. In this paper we present adaptive multi-view harmonium (SA-MVH) for multi-view feature extraction, where its each hidden node chooses the views to connect with while training phase via switch parameters. "Switch" parameters are multiplied to the connection weights of ordinary exponential family harmoniums (EFH) to decide the existence of connection between hidden nodes and views. With switch parameters, a SA-MVH automatically adapts its structure to achieve better representation of data distribution. The model can also be easily trained using the same training algorithms used for EFHs. Numerical experiments on synthetic and real-world datasets demonstrate the useful behavior of the SA-MVH, compared to the existing multi-view feature extraction methods.

Hashing seeks an embedding of high-dimensional objects into a similarity-preserving low-dimensional Hamming space such that similar objects are indexed by binary codes with small Hamming distances. A variety of hashing methods have been developed, but most of them resort to a single view (representation) of data. However, objects are often described by multiple representations. For instance, images are described by a few different visual descriptors (such as SIFT, GIST, and HOG), so it is desirable to incorporate multiple representations into hashing, leading to multi-view hashing. In this paper we present a deep network for multi-view hashing, referred to as deep multi-view hashing, where each layer of hidden nodes is composed of view-specific and shared hidden nodes, in order to learn individual and shared hidden spaces from multiple views of data. Numerical experiments on image datasets demonstrate the useful behavior of our deep multi-view hashing (DMVH), compared to recently-proposed multi-modal deep network as well as existing shallow models of hashing.