Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32135
Title: Ordinal regression via manifold learning
Authors: Liu, Y
Liu, Y 
Chan, KCC 
Issue Date: 2011
Source: Proceedings of the National Conference on Artificial Intelligence, 2011, v. 1, p. 398-403 How to cite?
Abstract: Ordinal regression is an important research topic in machine learning. It aims to automatically determine the implied rating of a data item on a fixed, discrete rating scale. In this paper, we present a novel ordinal regression approach via manifold learning, which is capable of uncovering the embedded nonlinear structure of the data set according to the observations in the high-dimensional feature space. By optimizing the order information of the observations and preserving the intrinsic geometry of the data set simultaneously, the proposed algorithm provides the faithful ordinal regression to the new coming data points. To offer more general solution to the data with natural tensor structure, we further introduce the multilinear extension of the proposed algorithm, which can support the ordinal regression of high order data like images. Experiments on various data sets validate the effectiveness of the proposed algorithm as well as its extension.
Description: 25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11, San Francisco, CA, 7-11 August 2011
URI: http://hdl.handle.net/10397/32135
ISBN: 9781577355083
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

13
Last Week
0
Last month
Citations as of Dec 6, 2017

Page view(s)

47
Last Week
1
Last month
Checked on Dec 11, 2017

Google ScholarTM

Check



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.