Nuclear Norm Based 2DPCA

2013 2nd IAPR Asian Conference on Pattern Recognition Pub Date : 2013-11-05 DOI:10.1109/ACPR.2013.10

Fanlong Zhang, J. Qian, Jian Yang

引用次数: 3

Abstract

This paper presents a novel method, namely nuclear norm based 2DPCA (N-2DPCA), for image feature extraction. Unlike the conventional 2DPCA, N-2DPCA uses a nuclear norm based reconstruction error criterion. The criterion is minimized by converting the nuclear norm based optimization problem into a series of F-norm based optimization problems. N-2DPCA is applied to face recognition and is evaluated using the Extended Yale B and CMU PIE databases. Experimental results demonstrate that our method is more effective and robust than PCA, 2DPCA and L1-Norm based 2DPCA.

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基于核规范的2DPCA

提出了一种基于核范数的2DPCA (N-2DPCA)图像特征提取方法。与传统的2DPCA不同，N-2DPCA使用基于核范数的重构误差准则。通过将基于核范数的优化问题转化为一系列基于f范数的优化问题，使准则最小化。N-2DPCA应用于人脸识别，并使用扩展耶鲁B和CMU PIE数据库进行评估。实验结果表明，该方法比PCA、2DPCA和基于L1-Norm的2DPCA具有更好的鲁棒性和有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2013 2nd IAPR Asian Conference on Pattern Recognition

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