Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141947
Title: Transferring knowledge fragments for learning distance metric from a heterogeneous domain
Authors: Luo, Yong
Wen, Yonggang
Liu, Tongliang
Tao, Dacheng
Keywords: Statistics - Machine Learning
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Learning
Engineering::Computer science and engineering
Issue Date: 2019
Source: Luo, Y., Wen, Y., Liu, T., & Tao, D. (2019). Transferring knowledge fragments for learning distance metric from a heterogeneous domain. IEEE transactions on pattern analysis and machine intelligence, 41(4), 1013 - 1026. doi:10.1109/TPAMI.2018.2824309
Project: NRF2015ENC-GDCR01001-003
NRF2015ENC-GBICRD001-012
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Abstract: The goal of transfer learning is to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. In this paper, we examine the problem of transfer distance metric learning (DML), which usually aims to mitigate the label information deficiency issue in the target DML. Most of the current Transfer DML (TDML) methods are not applicable to the scenario where data are drawn from heterogeneous domains. Some existing heterogeneous transfer learning (HTL) approaches can learn target distance metric by usually transforming the samples of source and target domain into a common subspace. However, these approaches lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. This motivates us to develop a general flexible heterogeneous TDML (HTDML) framework. In particular, any (linear/nonlinear) DML algorithms can be employed to learn the source metric beforehand. Then the pre-learned source metric is represented as a set of knowledge fragments to help target metric learning. We show how generalization error in the target domain could be reduced using the proposed transfer strategy, and develop novel algorithm to learn either linear or nonlinear target metric. Extensive experiments on various applications demonstrate the effectiveness of the proposed method.
URI: https://hdl.handle.net/10356/141947
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2018.2824309
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TPAMI.2018.2824309
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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