In:
International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Pub Co Pte Ltd, Vol. 22, No. 07 ( 2008-11), p. 1283-1299
Abstract:
Recently, Graph Embedding Framework has been proposed for feature extraction. However, it is still an open issue on how to compute robust discriminant transformation for this purpose. In this paper, we show that supervised graph embedding algorithms share a general criterion. Based on the analysis of this criterion, we propose a general solution, called General Solution for Supervised Graph Embedding (GSSGE), for extracting the robust discriminant transformation of Supervised Graph Embedding. Then, we analyze the superiority of our algorithm over traditional algorithms. Extensive experiments on both artificial and real-world data are performed to demonstrate the effectiveness and robustness of our proposed GSSGE.
Type of Medium:
Online Resource
ISSN:
0218-0014
,
1793-6381
DOI:
10.1142/S021800140800679X
Language:
English
Publisher:
World Scientific Pub Co Pte Ltd
Publication Date:
2008
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