Learning High-Dimensional Correspondence via Manifold Learning and Local Approximation
Chenping Hou, Feiping Nie, Hua Wang, Dongyun Yi, Changshui Zhang
Neural Computing and Applications - 2013
The recent years have witnessed a surge of interests of learning high-dimensional correspondence, which is important for both machine learning and neural computation community. Manifold learning–based researches have been considered as one of the most promising directions. In this paper, by analyzing traditional methods, we summarized a new framework for high-dimensional correspondence learning. Within this framework, we also presented a new approach, Local Approximation Maximum Variance Unfolding. Compared with other machine learning–based methods, it could achieve higher accuracy. Besides, we also introduce how to use the proposed framework and methods in a concrete application, crosssystem personalization (CSP). Promising experimental results on image alignment and CSP applications are proposed for demonstration.
Links
- View publications from Hua Wang
- View publications researching Embeddings
- View publications researching Graph Representations/Learning
Cite this paper
MLA
Hou, Chenping, et al. "Learning high-dimensional correspondence via manifold learning and local approximation." Neural Computing and Applications 24.7-8 (2014): 1555-1568.
BibTeX
@article{hou2014learning,
title={Learning high-dimensional correspondence via manifold learning and local approximation},
author={Hou, Chenping and Nie, Feiping and Wang, Hua and Yi, Dongyun and Zhang, Changshui},
journal={Neural Computing and Applications},
volume={24},
number={7-8},
pages={1555--1568},
year={2014},
publisher={Springer}
}