2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMMSP-1.2
Paper Title FEATURE INTEGRATION VIA SEMI-SUPERVISED ORDINALLY MULTI-MODAL GAUSSIAN PROCESS LATENT VARIABLE MODEL
Authors Kyohei Kamikawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, Hokkaido University, Japan
SessionMMSP-1: Multimedia Signal Processing
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Multimedia Signal Processing: Multimedia Applications
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.