Title :
Tensor Locally Linear Discriminative Analysis
Author :
Zhao Zhang ; Chow, W.S.
Author_Institution :
Dept. of Electron. Eng., City Univ. of Hong Kong, Kowloon, China
Abstract :
This letter presents a Tensor Locally Linear Discriminative Analysis (TLLDA) method for image presentation. TLLDA is originated from the Local Fisher Discriminant Analysis (LFDA), but TLLDA offers some advantages over LFDA. 1) TLLDA can preserve the local discriminative information of image data as LFDA. 2) TLLDA represents images as matrices or 2-order tensors rather than vectors, so TLLDA keeps the spatial locality of pixels in the images. 3) TLLDA avoids the singularity that may be suffered by LFDA. 4) TLLDA is faster than LFDA. Simulations on two real databases verified the validity of TLLDA. Results show that TLLDA is highly competitive with some widely used techniques.
Keywords :
image representation; tensors; 2-order tensors; LFDA; TLLDA method; image data; image presentation; local discriminative information; local fisher discriminant analysis; tensor locally linear discriminative analysis; Databases; Euclidean distance; Face; Feature extraction; Optimization; Principal component analysis; Tensile stress; Dimensionality reduction; discriminant analysis; tensor representation; trace ratio optimization;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2011.2165538
