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Multimedia Tools and Applications

, Volume 78, Issue 21, pp 30959–30973 | Cite as

Auto-weighted Mutli-view Sparse Reconstructive Embedding

  • Huibing Wang
  • Haohao Li
  • Xianping FuEmail author
Article

Abstract

With the development of multimedia era, multi-view data is generated in various fields. Contrast with those single-view data, multi-view data brings more useful information and should be carefully excavated. Therefore, it is essential to fully exploit the complementary information embedded in multiple views to enhance the performances of many tasks. Especially for those high-dimensional data, how to develop a multi-view dimension reduction algorithm to obtain the low-dimensional representations is of vital importance but chanllenging. In this paper, we propose a novel multi-view dimensional reduction algorithm named Auto-weighted Mutli-view Sparse Reconstructive Embedding (AMSRE) to deal with this problem. AMSRE fully exploits the sparse reconstructive correlations between features from multiple views. Furthermore, it is equipped with an auto-weighted technique to treat multiple views discriminatively according to their contributions. Various experiments have verified the excellent performances of the proposed AMSRE.

Keywords

Multi-view Sparse representation Auto-weighted Mutli-view Sparse Reconstructive Embedding Dimension reduction 

Notes

Compliance with Ethical Standards

Conflict of interests

This study was funded by the National Natural Science Foundation of China Grant 61370142 and Grant 61272368, by the Fundamental Research Funds for the Central Universities Grant 3132016352, by the Fundamental Research of Ministry of Transport of P.R. China Grant 2015329225300. Huibing Wang, Haohao Li and Xianping Fu declare that they have no conflict of interest. Both Huibing Wang and Haohao Li contribute equally to this paper. This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Information and Science TechnologyDalian Maritime UniversityDalianChina
  2. 2.School of Mathematical SciencesDalian University of TechnologyDalianChina

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