Learning vector quantization algorithm as classifier for Arabic handwritten characters recognition
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Features extraction method for Arabic characters based on pixel orientation technique
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Arabic Handwritten Characters Classification Using Learning Vector Quantization Algorithm
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
Palmprint recognition based on improved 2DPCA
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
Expert Systems with Applications: An International Journal
Future Generation Computer Systems
Separable linear discriminant analysis
Computational Statistics & Data Analysis
Weighted Modular Image Principal Component Analysis for face recognition
Expert Systems with Applications: An International Journal
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Principal component analysis (PCA) and linear discriminant analysis (LDA) are two important feature extraction methods and have been widely applied in a variety of areas. A limitation of PCA and LDA is that when dealing with image data, the image matrices must be first transformed into vectors, which are usually of very high dimensionality. This causes expensive computational cost and sometimes the singularity problem. Recently two methods called two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) were proposed to overcome this disadvantage by working directly on 2-D image matrices without a vectorization procedure. The 2DPCA and 2DLDA significantly reduce the computational effort and the possibility of singularity in feature extraction. In this paper, we show that these matrices based 2-D algorithms are equivalent to special cases of image block based feature extraction, i.e., partition each image into several blocks and perform standard PCA or LDA on the aggregate of all image blocks. These results thus provide a better understanding of the 2-D feature extraction approaches.