半监督转导判别分析

Yi Li, Xuesong Yin
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引用次数: 3

摘要

当没有足够的标记实例时,监督降维方法往往由于过度拟合而表现不佳。在这种情况下,使用未标记的实例来提高性能。在本文中,我们提出了一种称为半监督转导判别分析(TIDA)的降维方法,该方法除了将不同类别的标记实例相互分离外,还保留了未标记实例的全局和几何结构。该算法效率高,且具有封闭解。在广泛的数据集上进行的实验表明,TIDA算法优于许多相关的降维方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semi-supervised Transductive Discriminant Analysis
When there is no sufficient labeled instances, supervised dimensionality reduction methods tend to perform poorly due to overfitting. In such cases, unlabeled instances are used to improve the performance. In this paper, we propose a dimensionality reduction method called semi-supervised TransductIve Discriminant Analysis (TIDA) which preserves the global and geometrical structure of the unlabeled instances in addition to separating labeled instances in different classes from each other. The proposed algorithm is efficient and has a closed form solution. Experiments on a broad range of data sets show that TIDA is superior to many relevant dimensionality reduction methods.
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