Paper Info
Title
Fractional-Order Embedding Supervised Canonical Correlations Analysis with Applications to Feature Extraction and Recognition.
Abstract
Due to the noise disturbance and limited number of training samples, within-set and between-set sample covariance matrices in canonical correlations analysis (CCA) based methods usually deviate from the true ones. In this paper, we re-estimate the covariance matrices by embedding fractional order and incorporate the class label information. First, we illustrate the effectiveness of the fractional-order embedding model through theory analysis and experiments. Then, we quote fractional-order within-set and between-set scatter matrices, which can significantly reduce the deviation of sample covariance matrices. Finally, we incorporate the supervised information, novel generalized CCA and discriminative CCA are presented for multi-view dimensionality reduction and recognition, called fractional-order embedding generalized canonical correlations analysis and fractional-order embedding discriminative canonical correlations analysis. Extensive experimental results on various handwritten numeral, face and object recognition problems show that the proposed methods are very effective and obviously outperform the existing methods in terms of classification accuracy.
Year
DOI
Venue
2017
https://doi.org/10.1007/s11063-016-9524-z
Neural Processing Letters
Keywords
Field
DocType
Fractional-order,Pattern recognition,Singular values,Scatter matrix,Feature extraction
Dimensionality reduction,Embedding,Pattern recognition,Matrix (mathematics),Order embedding,Feature extraction,Artificial intelligence,Discriminative model,Scatter matrix,Machine learning,Mathematics,Covariance
Journal
Volume
Issue
ISSN
45
1
1370-4621
Citations 
PageRank 
References 
2
0.36
22
Authors
4
Name
Order
Citations
PageRank
Hongkun Ji1151.55
Quansen Sun2122283.09
Yunhao Yuan3194.64
Zexuan Ji4377.34