Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multimodal Subspace Support Vector Data Description   In this paper, we propose a novel method for projecting data from multiple
modalities to a new subspace optimized for one-class classification. The
proposed method iteratively transforms the data from the original feature space
of each modality to a new common feature space along with finding a joint
compact description of data coming from all the modalities. For data in each
modality, we define a separate transformation to map the data from the
corresponding feature space to the new optimized subspace by exploiting the
available information from the class of interest only. We also propose
different regularization strategies for the proposed method and provide both
linear and non-linear formulations. The proposed Multimodal Subspace Support
Vector Data Description outperforms all the competing methods using data from a
single modality or fusing data from all modalities in four out of five
datasets.

9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th