Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Multi-modal Hashing for Cross-modal retrieval   With the advantage of low storage cost and high efficiency, hashing learning
has received much attention in the domain of Big Data. In this paper, we
propose a novel unsupervised hashing learning method to cope with this open
problem to directly preserve the manifold structure by hashing. To address this
problem, both the semantic correlation in textual space and the locally
geometric structure in the visual space are explored simultaneously in our
framework. Besides, the `2;1-norm constraint is imposed on the projection
matrices to learn the discriminative hash function for each modality. Extensive
experiments are performed to evaluate the proposed method on the three publicly
available datasets and the experimental results show that our method can
achieve superior performance over the state-of-the-art methods.

28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph