2021年
Feature Integration via Semi-Supervised Ordinally Multi-Modal Gaussian Process Latent Variable Model.
IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP)
- ,
- ,
- ,
- 開始ページ
- 4130
- 終了ページ
- 4134
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICASSP39728.2021.9414109
- 出版者・発行元
- IEEE
This paper presents a method of feature integration via semi-supervised ordinally multi-modal Gaussian process latent variable model (Semi-OMGP). The proposed method transforms multi-modal features into common latent variables suitable for users' interest level estimation. For dealing with the multi-modal features, the proposed method newly derives Semi-OMGP. Semi-OMGP has two contributions. First, Semi-OMGP is suitable for integration between heterogeneous modalities with different distributions by assuming that the similarity matrices of these modalities as observations are generated from latent variables. Second, Semi-OMGP can efficiently use label information by introducing an operator considering the ordinal grade into the prior distribution of latent variables when obtained label information is partially given. Semi-OMGP can simultaneously realize the above contributions, and successful multi-modal feature integration becomes feasible. Experimental results show the effectiveness of the proposed method.
- リンク情報
-
- DOI
- https://doi.org/10.1109/ICASSP39728.2021.9414109
- DBLP
- https://dblp.uni-trier.de/rec/conf/icassp/KamikawaMOH21
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000704288404078&DestApp=WOS_CPL
- URL
- https://dblp.uni-trier.de/rec/conf/icassp/2021
- URL
- https://dblp.uni-trier.de/db/conf/icassp/icassp2021.html#KamikawaMOH21
- ID情報
-
- DOI : 10.1109/ICASSP39728.2021.9414109
- ISBN : 9781728176062
- ISBN : 9781728176055
- DBLP ID : conf/icassp/KamikawaMOH21
- Web of Science ID : WOS:000704288404078