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Supervised Linear Dimensionality Reduction with Robust Margins for Object Recognition

机译：具有鲁棒余量的监督线性降维用于对象识别

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摘要

Linear Dimensionality Reduction (LDR) techniques have been increasingly important in computer vision and pattern recognition since they permit a relatively simple mapping of data onto a lower dimensional subspace, leading to simple and computationally efficient classification strategies. Recently, many linear discriminant methods have been developed in order to reduce the dimensionality of visual data and to enhance the discrimination between different groups or classes. Many existing linear embedding techniques relied on the use of local margins in order to get a good discrimination performance. However, dealing with outliers and within-class diversity has not been addressed by margin-based embedding method. In this paper, we explored the use of different margin-based linear embedding methods. More precisely, we propose to use the concepts of Median miss and Median hit for building robust margin-based criteria. Based on such margins, we seek the projection directions (linear embedding) such that the sum of local margins is maximized. Our proposed approach has been applied to the problem of appearance-based face recognition. Experiments performed on four public face databases show that the proposed approach can give better generalization performance than the classic Average Neighborhood Margin Maximization (ANMM). Moreover, thanks to the use of robust margins, the proposed method downgrades gracefully when label outliers contaminate the training data set. In particular, we show that the concept of Median hit was crucial in order to get robust performance in the presence of outliers.
机译：线性降维（LDR）技术在计算机视觉和模式识别中已变得越来越重要，因为它们允许将数据相对简单地映射到低维子空间，从而导致简单且计算效率高的分类策略。近来，已经开发了许多线性判别方法，以减小视觉数据的维数并增强不同组或类别之间的区别。许多现有的线性嵌入技术都依靠使用局部边距来获得良好的判别性能。但是，基于边缘的嵌入方法尚未解决处理离群值和类内差异的问题。在本文中，我们探索了不同基于余量的线性嵌入方法的使用。更准确地说，我们建议使用中位数未中和中位数的概念来构建基于边距的可靠标准。基于这些边距，我们寻求投影方向（线性嵌入），以使局部边距的总和最大化。我们提出的方法已经应用于基于外观的面部识别问题。在四个公开面孔数据库上进行的实验表明，与经典的平均邻域余量最大化（ANMM）相比，该方法可提供更好的泛化性能。此外，由于使用了可靠的边距，当标签离群值污染了训练数据集时，所提出的方法将降级。特别是，我们证明了中值匹配的概念对于在异常值存在时获得强大的性能至关重要。

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