|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM286367203 |
| 003 |
DE-627 |
| 005 |
20250223192136.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231225s2019 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2018.2794976
|2 doi
|
| 028 |
5 |
2 |
|a pubmed25n0954.xml
|
| 035 |
|
|
|a (DE-627)NLM286367203
|
| 035 |
|
|
|a (NLM)29994178
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Liu, Weiwei
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Metric Learning for Multi-Output Tasks
|
| 264 |
|
1 |
|c 2019
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 20.11.2019
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Multi-output learning with the task of simultaneously predicting multiple outputs for an input has increasingly attracted interest from researchers due to its wide application. The k nearest neighbor ([Formula: see text]) algorithm is one of the most popular frameworks for handling multi-output problems. The performance of [Formula: see text] depends crucially on the metric used to compute the distance between different instances. However, our experiment results show that the existing advanced metric learning technique cannot provide an appropriate distance metric for multi-output tasks. This paper systematically studies how to efficiently learn an appropriate distance metric for multi-output problems with provable guarantee. In particular, we present a novel large margin metric learning paradigm for multi-output tasks, which projects both the input and output into the same embedding space and then learns a distance metric to discover output dependency such that instances with very different multiple outputs will be moved far away. Several strategies are then proposed to speed up the training and testing time. Moreover, we study the generalization error bound of our method for three learning tasks, which shows that our method converges to the optimal solutions. Experiments on three multi-output learning tasks (multi-label classification, multi-target regression, and multi-concept retrieval) validate the effectiveness and scalability of the proposed method
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Xu, Donna
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Tsang, Ivor W
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Zhang, Wenjie
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 41(2019), 2 vom: 09. Feb., Seite 408-422
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
|
| 773 |
1 |
8 |
|g volume:41
|g year:2019
|g number:2
|g day:09
|g month:02
|g pages:408-422
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2018.2794976
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 41
|j 2019
|e 2
|b 09
|c 02
|h 408-422
|