Exploiting Known Taxonomies in Learning Overlapping Concepts
Exploiting Known Taxonomies in Learning Overlapping Concepts
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发表时间：
2007-01
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通讯作者：
Deng Cai;Xiaofei He;Kun Zhou;Jiawei Han;H. Bao
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作者：
Deng Cai;Xiaofei He;Kun Zhou;Jiawei Han;H. Bao
线性判别分析(LDA)是一种流行的数据分析工具，用于研究数据点之间的类别关系。LDA的一个主要缺点是不能发现数据流形的局部几何结构。本文介绍了一种新的线性判别分析算法，称为局部敏感判别分析(LSDA)。当没有足够的训练样本时，对于判别分析，局部结构通常比全局结构更重要。通过发现局部流形结构，LSDA找到一个投影，使每个局部区域中不同类别的数据点之间的差值最大化。具体地说，将数据点映射到一个子空间中，在该子空间中，具有相同标签的邻近点彼此接近，而具有不同标签的邻近点相距较远。在几个标准的人脸数据库上进行的实验表明，与基于LDA的识别结果相比，识别结果有明显的改善。
Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the class relationship between data points. A major disadvantage of LDA is that it fails to discover the local geometrical structure of the data manifold. In this paper, we introduce a novel linear algorithm for discriminant analysis, called Locality Sensitive Discriminant Analysis (LSDA). When there is no sufficient training samples, local structure is generally more important than global structure for discriminant analysis. By discovering the local manifold structure, LSDA finds a projection which maximizes the margin between data points from different classes at each local area. Specifically, the data points are mapped into a subspace in which the nearby points with the same label are close to each other while the nearby points with different labels are far apart. Experiments carried out on several standard face databases show a clear improvement over the results of LDA-based recognition.