The advance of deep learning has shown great potential in applications (speech, image,
and video classification). In these applications, deep learning models are trained by …

Recent vision and learning studies show that learning compact hash codes can facilitate
massive data processing with significantly reduced storage and computation. Particularly …

Despite its great success, matrix factorization based cross-modality hashing suffers from two
problems: 1) there is no engagement between feature learning and binarization; and 2) most …

This paper proposes a new hashing framework to conduct similarity search via the following
steps: first, employing linear clustering methods to obtain a set of representative data points …

With the increasing demand of massive multimodal data storage and organization, cross-
modal retrieval based on hashing technique has drawn much attention nowadays. It takes …

There are many successful spectral based unsupervised dimensionality reduction methods,
including Laplacian Eigenmap (LE), Locality Preserving Projection (LPP), Spectral …

Recently, graph-based hashing that learns similarity-preserving binary codes via an affinity
graph has been extensively studied for large-scale image retrieval. However, most graph …

Generally, the existing graph-based multi-view clustering models consists of two steps:(1)
graph construction;(2) eigen-decomposition on the graph Laplacian matrix to compute a …

Compact hash codes can facilitate large-scale multimedia retrieval, significantly reducing
storage and computation. Most hashing methods learn hash functions based on the data …

Spectral clustering has been regarded as a powerful tool for unsupervised tasks despite its
excellent performance, the high computational cost has become a bottleneck which limits its …