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|Title:||Incremental PCA-LDA Algorithm||Authors:||Dagher, Issam||Affiliations:||Department of Computer Engineering||Keywords:||Recursive PCA-LDA
Principal component analysis (PCA)
Linear discriminant analysis (LDA)
|Issue Date:||2010||Part of:||International journal of biometrics and bioinformatics||Volume:||4||Issue:||2||Start page:||86||End page:||99||Abstract:||
In this paper a recursive algorithm of calculating the discriminant features of the PCA-LDA procedure is introduced. This algorithm computes the principal components of a sequence of vectors incrementally without estimating the covariance matrix (so covariance-free) and at the same time computing the linear discriminant directions along which the classes are well separated. Two major techniques are used sequentially in a real time fashion in order to obtain the most efficient and linearly discriminative components. This procedure is done by merging the runs of two algorithms based on principal component analysis (PCA) and linear discriminant analysis (LDA) running sequentially. This algorithm is applied to face recognition problem. Simulation results on different databases showed high average success rate of this algorithm compared to PCA and LDA algorithms. The advantage of the incremental property of this algorithm compared to the batch PCA-LDA is also shown.
This paper was presented in "IEEE international Conference on Computational Intelligence For Measurements Systems and Applications (CIMSA 2010), September 6-8, 2010, . Taranto, Italy".
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/2126||Open URL:||Link to full text||Type:||Journal Article|
|Appears in Collections:||Department of Computer Engineering|
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