Abstract
A Mahalanobis distance based semi-supervised fuzzy clustering model is presented in this paper, whose objective function has a good explanation on how the labeled and unlabeled data are used in finding the underlying structure of matrix data. The iterative algorithm to solve this model is given. This algorithm can directly deal with matrix data like face images. We use 2DPCA on both row and column directions to reduce the dimension of image faces. The experimental result shows that using 2DPCA and semi-supervised algorithms can have a fairly good recognition rate if enough labeled data are given.
Full Text 
Sign-in/Register to access full text options
Sign-in/Register to access full text options 
Published version (
Free)
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
