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 IDMLSP-44.2
Paper Title MULTI-MODAL LABEL DEQUANTIZED GAUSSIAN PROCESS LATENT VARIABLE MODEL FOR ORDINAL LABEL ESTIMATION
Authors Masanao Matsumoto, Keisuke Maeda, Hokkaido University, Japan; Naoki Saito, National Institute of Technology, Kushiro College, Japan; Takahiro Ogawa, Miki Haseyama, Hokkaido University, Japan
SessionMLSP-44: Multimodal Data and Applications
LocationGather.Town
Session Time:Friday, 11 June, 13:00 - 13:45
Presentation Time:Friday, 11 June, 13:00 - 13:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-LMM] Learning from multimodal data
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper presents multi-modal label dequantized Gaussian process latent variable model (mLDGP) for ordinal label estimation. mLDGP is constructed based on a probabilistic generative model via Gaussian process and realizes accurate calculation of common latent space from multi-view features including low-dimensional ordinal label features. Conventional methods have a problem that the dimension of the common latent space was limited to that of the label feature, and an enough expressive latent space cannot be obtained. mLDGP, which is constructed by introducing our novel label dequantization mechanism into the objective function of multi-modal Gaussian process latent variable model (GPLVM), can increase the dimension of label features. Then mLDGP can calculate the effective latent space. Furthermore, mLDGP can estimate projection transforming unknown features of test samples into the common latent space, which was a problem of the conventional GPLVMs. From experimental results obtained by applying our method to the product rating estimation on the online shopping website, it is confirmed that accuracy improvement using mLDGP becomes feasible compared to various methods.