.png){kind=icon}
.png){kind=icon}
Return
Multimodal subspace support vector data description
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2020.107648.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we propose a novel method for projecting data from multiple modalities to a new sub-space 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. (c) 2020 The Authors. Published by Elsevier Ltd.
Keywords:
Feature transformation
Multimodal data
One-class classification
Support vector data description
Subspace learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W