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Large margin projection-based multi-metric learning for classification

KNOWLEDGE-BASED SYSTEMS 2022年 243卷 108481-108481页

作者： Yuan, Chao Yang, Liming China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China China Agr Univ Coll Sci Beijing 100083 Peoples R China

Metric learning has been a promising technology to improve classification performance, which aims to learn a data-dependent distance metric such that the similarity between samples can be more effectively evaluated. Metric plays a significant role in the description of similarity between samples, however, learning a single distance metric is usually inadequate, especially when dealing with the heterogeneously distributed data. Traditional metric learning only considers a global metric, while the local metric, which is critical for heterogeneous data, is ignored. In this paper, we formulate a novel large margin projection-based multi-metric learning (LMML) for the binary classification of heterogeneous data, which constructs a unified framework based on global metric and local metrics, where two local distance metrics are learned, one for each class, so that the covariance of samples is as small as possible, and the sample of another class is as far away as possible from the mean of the sample. Moreover, a global distance metric is introduced to capture the common structure between the two classes, which requires that the distance metric in each class should be as close as possible to the global one. An efficient iterative algorithm is designed to optimize the LMML. We also conduct some insightful analyses on the computational complexity and the convergence of the proposed algorithm. Experiments are conducted on artificial datasets, UCI benchmark datasets and handwritten digit datasets to evaluate the proposed method. Compared with the state-of-the-art approaches, the experiment results demonstrate the feasibility and effectiveness of the proposed method. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Classification heterogeneously distributed data Multi-metric learning Iteration algorithm

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An efficient method for clustered multi-metric learning

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INFORMATION SCIENCES 2019年 471卷 149-163页

作者： Bac Nguyen Ferri, Francesc J. Morell, Carlos De Baets, Bernard Univ Ghent Dept Data Anal & Math Modeling KERMIT Coupure Links 653 B-9000 Ghent Belgium Univ Valencia Dept Informat E-46100 Valencia Spain Univ Cent Marta Abreu Villas Comp Sci Dept Santa Clara Cuba

Distance metric learning, which aims at finding a distance metric that separates examples of one class from examples of the other classes, is the key to the success of many machine learning tasks. Although there has been an increasing interest in this field, learning a global distance metric is insufficient to obtain satisfactory results when dealing with heterogeneously distributed data. A simple solution to tackle this kind of data is based on kernel embedding methods. However, it quickly becomes computationally intractable as the number of examples increases. In this paper, we propose an efficient method that learns multiple local distance metrics instead of a single global one. More specifically, the training examples are divided into several disjoint clusters, in each of which a distance metric is trained to separate the data locally. Additionally, a global regularization is introduced to preserve some common properties of different clusters in the learned metric space. By learning multiple distance metrics jointly within a single unified optimization framework, our method consistently outperforms single distance metric learning methods, while being more efficient than other state-of-the-art multi-metric learning methods. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Multi-metric learning heterogeneously distributed data Nearest-neighbor classification Convex optimization

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