Multi-Modal Label Dequantized Gaussian Process Latent Variable Model for Ordinal Label Estimation

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.

Trial Analysis of the Relationship between Taste and Biological Information Obtained While Eating Strawberries for Sensory Evaluation

“…Therefore, we will also increase the number of dimensions of features expressing taste by adding evaluations other than VAS and will calculate more reliable canonical correlations. As a solution to this problem, multivariate analysis models that introduce label dequantization have been proposed [ 48 , 49 ], and their use will be considered in future work. In addition, while general CCA is based on two types of data, methods based on multi-view CCA are effective when various types of data are used [ 50 , 51 ], as in our experiment in this paper.…”

Gaussian Distributed Graph Constrained Multi-Modal Gaussian Process Latent Variable Model for Ordinal Labeled Data

“…With the spread of the Internet, several services such as Netflix 1 and Amazon 2 are expanding market shares, and multi-modal data analysis can be also applied to these applications. For example, product descriptions, prices and package images can be used to accurately understand the user's preference (i.e., rating) toward the product, and analyzing them can lead to advanced product recommendation [5,6]. On the other hand, since these services focus on personalized recommendations, the rating given by users in the past is an important factor for constructing recommendation systems, but the amount of rating data is much smaller than that of multimedia data on the Web.…”