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Tensor Locally Linear Discriminative Analysis
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DOI：10.1109/LSP.2011.2165538.png)
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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.
Keyword:
Dimensionality reduction
discriminant analysis
tensor representation
trace ratio optimization
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9.6
论文数:
1.1W
被引数:
1.7W
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Extracting the optimal dimensionality for local tensor discriminant analysis.png)
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