Discriminative Subspace Method for Minimum Error Pattern Recognition
Discriminative Subspace Method for Minimum Error Pattern Recognition
复制标题
最小错误模式识别的判别子空间方法
DOI：
10.1109/nnsp.1995.514881
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
S. Katagiri
中科院分区：
文献类型：
--
作者：
H. Watanabe;S. Katagiri
子空间方法是模式识别的基本框架之一。特别是，它的辨别性学习版本，称为学习子空间方法(LSM)，已被证明在各种应用中非常有用。然而，由于LSM与模式识别的最终目标，即最小误差情形之间缺乏联系，这一重要的设计方法留下了很大的进一步分析空间。鉴于此，我们从最小分类误差/广义概率下降方法(MCE/GPD)的角度对SM进行了研究。将MCE/GPD应用到SM中，我们形式化了一种新的判别子空间方法，称为最小误差学习子空间方法(MELS)，它使人们能够直接追求最小误差识别。文中还对MELS的学习机制进行了严格的分析，并与传统的LSM和MELS进行了比较。
Subspace Method (SM) is one of fundamental frameworks for pattern recognition. In particular, its discriminative learning version, called Learning Subspace Method (LSM), has been shown quite useful in various applications. However, this important design method leaves much room for further analysis due to the lack of a link between LSM and the ultimate goal of pattern recognition, i.e. the minimum error situation. In this light, we investigate in this paper SM from the viewpoint of the Minimum Classification Error/Generalized Probabilistic Descent method (MCE/GPD). Applying MCE/GPD to SM, we formalize a new discriminative subspace method, called the Minimum Error Learning Subspace method (MELS), which enables one to directly pursue the minimum error recognition. This paper also provides a rigorous analysis of the MELS’s learning mechanism as well as a comparison between the conventional LSM and MELS.