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A Brief History of the Subspace Methods

机译:子空间方法简史

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摘要

I hope to start from one question. "Is the eigenface[l] a subspace method?"Answer is weakly YES and strongly NO. In wide meaning in Subspace method of pattern recognition is that uses subspace. In this meaning the answer is YES. However in narrow meaning the term "Subspace method" means pattern recognition techniques that represent class featuring information with subspace of original feature space [2]. The eigenface subspace represent common feature of trained faces, that is differ from class information. Thus in this meaning the answer is NO.For understanding the term of "Subspace method", we shall trace back to a Subspace method root. In this article I try to clarify the meaning of Subspace method through the historical study. To this goal we trace histories of Subspace methods from their birth at 1960s to 21c. We studied the history both side of theory and applications, because sometimes new theory is inspired by new application and new theory extend applicability of Subspace methods.The history of Subspace method is classified in three epochs.First epoch is the birth of Subspace methods, from '60th to '70th. Subspace method was originated by two Japanese researcher Prof. Taizo Iijima and Prof. Satoshi Watanabe independently. Prof. Iijima try to formulate an observation theory of object that include scale space methods[4]. Prof. Watanabe started from the information theory and the theory of probabilistic logics [5]. Interestingly they reached same goal from other start points. Their results are "categories or class information is represented by subspaces".Second epoch is the age of the application to character recognition and discriminative Subspace methods. Main issue of pattern recognition research in this age is character recognition[6]. Especially Japanese Kanji recognition problem was very important industrial problem in Japan. For obtaining high recognition accuracy, many discriminative Subspace methods were proposed [7]Third epoch was starting from Yamaguch et. al [8]. They demonstrate the effectiveness of mutual Subspace method for object recognition problem. From their paper, Subspace method is defined important technology of object recognition problem, and many improvement and extension were proposing[9,10,11,12]. Many other applications were proposed[13] in this epoch.Prom this historical study, we try to discuss current status and future issue of Subspace method.
机译:我希望从一个问题开始。 “本征面[l]是子空间方法吗?” 答案是“是”而“是”。在子空间中,广义的模式识别方法是使用子空间。在这个意义上,答案是“是”。但是,狭义的术语“子空间方法”是指模式识别技术,该技术代表具有原始特征空间子空间[2]的特征信息的类。特征脸子空间代表训练的脸部的共同特征,与类别信息不同。因此,从这个意义上讲,答案是否定的。 为了理解“子空间方法”的术语,我们将追溯到子空间方法的根。在本文中,我试图通过历史研究来阐明子空间方法的含义。为此,我们追溯了子空间方法从1960年代诞生到21c的历史。我们研究了理论和应用的历史,因为有时新理论受到新应用的启发,新理论扩展了子空间方法的适用性。 子空间方法的历史分为三个时期。 第一个时代是Subspace方法的诞生,从60年代到70年代。子空间方法是由两位日本研究人员饭岛泰三教授和渡边智俊教授分别提出的。 Iijima教授试图建立包括尺度空间方法在内的物体观测理论[4]。渡边教授从信息理论和概率逻辑理论开始[5]。有趣的是,他们从其他起点达到了相同的目标。他们的结果是“类别或类别信息由子空间表示”。 第二个时代是字符识别和区分性子空间方法的应用时代。在这个时代,模式识别研究的主要问题是字符识别[6]。特别是日本汉字识别问题是日本非常重要的工业问题。为了获得较高的识别精度,提出了许多区分性的子空间方法[7]。 第三个时代是从Yamaguch等人开始的。等[8]。他们证明了相互子空间方法对物体识别问题的有效性。从他们的论文来看,子空间方法被定义为对象识别问题的重要技术,并提出了许多改进和扩展[9,10,11,12]。在这一时期,提出了许多其他的应用[13]。 为了进行这一历史研究,我们尝试讨论子空间方法的现状和未来问题。

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