Multi-kernel Partial Least Squares for Multi-Modal Data Analysis

DOI

10.2991/emcm-16.2017.177How to use a DOI?

Keywords

Partial least squares regression; Multi-kernel learning; Multi-modal classification; Multi-modal retrieval; Canonical correlation analysis

Abstract

In recent years, multi-modal data analysis has enjoyed an increasing attention. Multi-modal data mean the different modal data representing the same semantics. Moreover, many subspace learning methods are proposed to measure the correlation between different modal data. As the most representative subspace learning method, canonical correlation analysis (CCA) and its variants project different modal data into a common space where the Pearson correlation is maximized. Yet CCA often causes information loss when switching the modals, and as a result, the partial least squares regression (PLSR) model is adopted to handle the problem. Subsequently, considering the nonlinearity of data, the kernel partial least squares regression (KPLSR) is proposed. Besides, KPLSR mostly relies on the kernel parameters. Hence, we propose to apply multi-kernel partial least squares regression (MKPLSR) for multi-modal data analysis. To evaluate the proposed approach, massive experiments are carried out. Compared with previous methods, the experimental results on two benchmark datasets composed of images and texts pairs, show the effectiveness of our approach, when applied to multi-modal data retrieval and classification.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ping Wang
AU  - Hong Zhang
PY  - 2017/02
DA  - 2017/02
TI  - Multi-kernel Partial Least Squares for Multi-Modal Data Analysis
BT  - Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/emcm-16.2017.177
DO  - 10.2991/emcm-16.2017.177
ID  - Wang2017/02
ER  -