基于贝叶斯正则化非负矩阵分解的人脸特征学习

Xueyi Zhao
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摘要

提出了一种基于Itakura-Saito (IS)散度(B-NMF)的贝叶斯正则化非负矩阵分解人脸特征学习方法。在本文中,我们表明,所提出的技术不仅明确地结合了“贝叶斯正则化先验”的概念,该概念施加于特征学习,而且还具有尺度不变性,使得学习过程中的低能量分量与高能分量同等重要。已进行了实际试验,取得了令人鼓舞的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian regularized nonnegative matrix factorization based face features learning
This paper proposes a novel technique for learning face features based on Bayesian regularized non-negative matrix factorization with Itakura-Saito (IS) divergence (B-NMF). In this paper, we show, the proposed technique not only explicitly incorporates the notion of ‘Bayesian regularized prior’ which imposes onto the features learning but also holds the property of scale invariant that enables lower energy components in the learning process to be treated with equal importance as the high energy components. Real test has been conducted and the obtained results are very encouraging.
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