A Robust and Efficient Doubly Regularized Metric Learning Approach.
A Robust and Efficient Doubly Regularized Metric Learning Approach.
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DOI：
10.1007/978-3-642-33765-9_46
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Vemuri, Baba C.
中科院分区：
文献类型：
--
作者：
Liu, Meizhu;Vemuri, Baba C.
在分类、图像检索、人脸识别等许多计算机视觉和模式识别应用中，适当的距离度量是基础。然而，对于特定的应用程序来说，通常不清楚什么度量标准是合适的，因此学习面向任务的度量标准变得更加可靠。多年来，文献中报道了许多度量学习方法。一种典型的方法是学习一个由正半定矩阵M参数化的马氏距离，一种有效的估计M的方法是将M视为一阶矩阵的线性组合，可以使用提升型方法学习。然而，这种方法有两个主要缺点。首先，训练样本间的权值变化可能不平滑。其次，学习到的第一阶矩阵可能是冗余的。在本文中，我们提出了一种双正则化度量学习算法，称为DRMetric，它在传统的度量学习方法上施加了两个正则化。首先，对训练样例的权值进行正则化，防止了权值的不稳定变化，也防止了离群样例的权值过大。此外，对秩一矩阵进行正则化，使它们相互独立。这大大减少了秩一矩阵的冗余。我们提出的实验描述了所提出的方法在各种应用的各种数据集上的性能。
A proper distance metric is fundamental in many computer vision and pattern recognition applications such as classification, image retrieval, face recognition and so on. However, it is usually not clear what metric is appropriate for specific applications, therefore it becomes more reliable to learn a task oriented metric. Over the years, many metric learning approaches have been reported in literature. A typical one is to learn a Mahalanobis distance which is parameterized by a positive semidefinite (PSD) matrix M. An efficient method of estimating M is to treat M as a linear combination of rank-one matrices that can be learned using a boosting type approach. However, such approaches have two main drawbacks. First, the weight change across the training samples maybe non-smooth. Second, the learned rank-one matrices might be redundant. In this paper, we propose a doubly regularized metric learning algorithm, termed by DRMetric, which imposes two regularizations on the conventional metric learning method. First, a regularization is applied on the weight of the training examples, which prevents unstable change of the weights and also prevents outlier examples from being weighed too much. Besides, a regularization is applied on the rank-one matrices to make them independent. This greatly reduces the redundancy of the rank-one matrices. We present experiments depicting the performance of the proposed method on a variety of datasets for various applications.
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DOI：
10.1023/b:visi.0000029664.99615.94
发表时间：
2004-11-01
影响因子：
19.5
作者：
Lowe, DG
通讯作者：
Lowe, DG
影响因子：
7.5
作者：
Demiriz, A;Bennett, KP;Shawe-Taylor, J
通讯作者：
Shawe-Taylor, J
影响因子：
7.5
作者：
Schapire, RE;Singer, Y
通讯作者：
Singer, Y
DOI：
10.1109/34.531803
发表时间：
1996-08-01
影响因子：
23.6
作者：
Manjunath, BS;Ma, WY
通讯作者：
Ma, WY
DOI：
10.1109/tpami.1986.4767851
发表时间：
1986-11-01
影响因子：
23.6
作者：
CANNY, J
通讯作者：
CANNY, J