The full text of this article

 

Self-adaptive local Fisher discriminant analysis for semi-supervised image recognition
by Zhonghua Liu; Jingyan Wang; Jiaju Man; Yongping Li; Xinge You; Chao Wang
International Journal of Biometrics (IJBM), Vol. 4, No. 4, 2012

 

Abstract: In this paper, we present a self-adaptive semi-supervised dimension reduction framework for image class recognition. Compared with the Semi-supervised Local Fisher Discriminant Analysis (SELF), whose classification performance is significantly influenced by some parameters - Neighbour size k of every labelled sample, Affinity Matrix A = {Aij} and Trade-off parameter β, our sparse algorithm is developed based on the distribution of the data set, which is much more adaptive to data set themselves. To develop a more tractable and practical approach, we in particular impose neighourhood structure constraint on the labelled samples in the minimum reconstruction criterion and develop a quadratic optimisation technique to approximately estimate the affine matrix used in the Local Fisher Discriminant Analysis (LFDA). We also give a novel approach to estimate the β automatically. Our experiments on semi-supervised face recognition task demonstrate that the proposed method is more robust and efficient in dealing with the semi-supervised problems in face recognition when compared with the related SELF methods

Online publication date: Sun, 14-Oct-2012

 

is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

 
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

 
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biometrics (IJBM):
Login with your Inderscience username and password:

 

    Username:        Password:         

Forgotten your password?


 
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

 
If you still need assistance, please email subs@inderscience.com