A Scalable Kernel-Based Semisupervised Metric Learning Algorithm with Out-of-Sample Generalization Ability

Yeung, Dit–Yan; Chang, Hong; Dai, Guang

doi:10.1162/neco.2008.05-07-528

Cited by 15 publications

(3 citation statements)

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“…In order to deliver satisfactory results, finding a good distance metric for the problem at hand often plays a very crucial role. As such, metric learning (Xing et al, 2002) has received much attention in the research community (Baxter, 1997;Xing et al, 2002;Chang & Yeung, 2004;Weinberger et al, 2005;Davis et al, 2007;Yeung & Chang, 2007;Chen et al, 2007;Davis & Dhillon, 2008;Yeung et al, 2008;Zhan et al, 2009;Jin et al, 2009). Many metric learning methods have been proposed.…”

Section: Application To Transfer Metric Learningmentioning

confidence: 99%

A probabilistic framework for learning task relationships in multi-task learning

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Section: Application To Transfer Metric Learningmentioning

confidence: 99%

A probabilistic framework for learning task relationships in multi-task learning

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“…Consequently, the optimal kernel represents the desired characteristics. As an example, [95] proposed to devise a kernel matrix such that the squared Euclidean distance between pairs in the same class in the feature space is reduced. One important approach in this family that investigates the geometric representation of the mapped points in the feature space is considered in [4].…”

Section: Feature Space Conditionsmentioning

confidence: 99%

A survey of the state of the art in learning the kernels

2011

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In recent years, the machine learning community has witnessed a tremendous growth in the development of kernel-based learning algorithms. However, the performance of this class of algorithms greatly depends on the choice of the kernel function. Kernel function implicitly represents the inner product between a pair of points of a dataset in a higher dimensional space. This inner product amounts to the similarity between points and provides a solid foundation for nonlinear analysis in kernel-based learning algorithms. The most important challenge in kernel-based learning is the selection of an appropriate kernel for a given dataset. To remedy this problem, algorithms to learn the kernel have recently been proposed. These methods formulate a learning algorithm that finds an optimal kernel for a given dataset. In this paper, we present an overview of these algorithms and provide a comparison of various approaches to find an optimal kernel. Furthermore, a list of pivotal issues that lead to efficient design of such algorithms will be presented.

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“…In order to deliver satisfactory results, finding a good distance metric for the problem at hand often plays a very crucial role. As such, metric learning [25] has received much attention in the research community [12,25,5,22,8,26,6,7,27,29,13]. Many metric learning methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Transfer metric learning by learning task relationships

Zhang

Yeung

2010

Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Distance metric learning plays a very crucial role in many data mining algorithms because the performance of an algorithm relies heavily on choosing a good metric. However, the labeled data available in many applications is scarce and hence the metrics learned are often unsatisfactory. In this paper, we consider a transfer learning setting in which some related source tasks with labeled data are available to help the learning of the target task. We first propose a convex formulation for multi-task metric learning by modeling the task relationships in the form of a task covariance matrix. Then we regard transfer learning as a special case of multitask learning and adapt the formulation of multi-task metric learning to the transfer learning setting for our method, called transfer metric learning (TML). In TML, we learn the metric and the task covariances between the source tasks and the target task under a unified convex formulation. To solve the convex optimization problem, we use an alternating method in which each subproblem has an efficient solution. Experimental results on some commonly used transfer learning applications demonstrate the effectiveness of our method.

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A Scalable Kernel-Based Semisupervised Metric Learning Algorithm with Out-of-Sample Generalization Ability

Cited by 15 publications

References 10 publications

A probabilistic framework for learning task relationships in multi-task learning

A probabilistic framework for learning task relationships in multi-task learning

A survey of the state of the art in learning the kernels

Transfer metric learning by learning task relationships

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