| Paper ID | MLSP-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 |
| Session | MLSP-44: Multimodal Data and Applications |
| Location | Gather.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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
