Name
Abstract | ||
|---|---|---|
A novel model is proposed to learn combined distance and similarity measure (CDSM) based on triplet-based constraints.Our CDSM can be kernelized for learning nonlinear measures.Two effective strategies are suggested to speed up the training and testing of kernelized CDSM.Extensive experiments validate the superiority of CDSM and kernelized CDSM. Distance and similarity measures usually are complementary to pattern classification. With pairwise constraints, several approaches have been proposed to combine distance and similarity measures. However, it remains less investigated to use triplets of samples for joint learning of distance and similarity measures. Moreover, the kernel extension of triplet-based model is also nontrivial and computationally expensive. In this paper, we propose a novel method to learn a combined distance and similarity measure (CDSM). By incorporating with the max-margin model, we suggest a triplet-based CDSM learning model with a unified regularizer of the Frobenius norm. A support vector machine (SVM)-based algorithm is then adopted to solve the optimization problem. Furthermore, we extend CDSM for learning nonlinear measures via the kernel trick. Two effective strategies are adopted to speed up training and testing of kernelized CDSM. Experiments on the UCI, handwritten digits and person re-identification datasets demonstrate that CDSM and kernelized CDSM outperform several state-of-the-art metric learning methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.ins.2017.04.027 | Inf. Sci. |
Keywords | Field | DocType |
Metric learning,Support vector machine,Kernel function,Max-margin model | Kernel (linear algebra),Pairwise comparison,Similarity measure,Pattern recognition,Support vector machine,Matrix norm,Artificial intelligence,Kernel method,Optimization problem,Machine learning,Mathematics,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
406 | C | 0020-0255 |
Citations | PageRank | References |
2 | 0.36 | 40 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mu Li | 1 | 958 | 66.10 |
| Qilong Wang | 2 | 150 | 15.49 |
| david zhang | 3 | 445 | 30.69 |
| Peihua Li | 4 | 466 | 37.53 |
| Wangmeng Zuo | 5 | 3833 | 173.11 |