Applied Math Seminar

04/20/2017 - 11:00am to 12:00pm
POT 745
Speaker(s) / Presenter(s): 
Jing Wei

Master Presentation

Title: Two-Dimensional PCA with F-Norm Minimization

Abstract: Two-dimensional principle component analysis (2DPCA) has been widely used for face image representation and recognition. But it is sensitive to the presence of outliers. To alleviate this problem, we propose a novel robust 2DPCA, namely 2DPCA with F-norm minimization (F-2DPCA), which is intuitive and directly derived from 2DPCA. In F-2DPCA, distance in spatial dimensions (attribute dimensions) is measured in F-norm, while the summation over different data points uses 1-norm. Thus it is robust to outliers and rotational invariant as well. To solve F-2DPCA, we propose a fast iterative algorithm, which has a closed-form solution in each iteration, and prove its convergence. Experimental results on faceĀ  image databases illustrate its effectiveness and advantages.

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