Distributed Label Dequantized Gaussian Process Latent Variable Model for Multi-View Data Integration
Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
Abstract
In this paper, we present a novel method for multi-view data analysis, distributed label dequantized Gaussian process latent variable model (DLDGP). DLDGP can integrate multi-view data and class information into a common latent space. In the previous multiview methods, the dimension of label features transformed from the class information is much smaller than those of the other modalities, which causes a dimensionality-limitation problem in the latent space. DLDGP extends the dimension of the label features by a distributed label dequantization scheme. Additionally, DLDGP calculates correlation between different classes by encoding class information into distributed features. DLDGP can correctly capture the relationship between multi-view data and obtain the latent features with high expression ability. Experimental results show the effectiveness of our method by using the open dataset.
BibTeX
@inproceedings{icassp2022_distributedlabel,
title = {Distributed Label Dequantized Gaussian Process Latent Variable Model for Multi-View Data Integration},
author = {Koshi Watanabe and Keisuke Maeda and Takahiro Ogawa and Miki Haseyama},
booktitle = {ICASSP 2022},
year = {2022}
}