This paper presents a method of feature integration via semisupervised ordinally multi-modal Gaussian process latent variable model (Semi-OMGP). The proposed method transforms multimodal 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.
This study presents a novel feature integration method for interest level estimation using a semi-supervised multimodal Gaussian process latent variable model with pseudo-labels (semi-MGPPL). Semi-MGPPL is an extended version of the multimodal Gaussian process latent variable model (mGPLVM). It integrates features calculated from multiple modalities to predict the users' interest levels in content. It is known that reflecting known interest levels of known users in the latent space effectively improves the accuracy of interest level estimation. However, previous methods have difficulty reflecting the interest levels when the number of samples is insufficient. Semi-MGPPL efficiently reflects interest levels in the latent space by pseudo-labeling of unlabeled samples and increasing the number of available pairs among labeled samples. In addition, obtaining behavior features is difficult for a new test sample. However, requirement of features of all modalities by previous mGPLVM-based methods makes the calculation of latent variables of a test sample challenging. Semi-MGPPL solves this problem by training a projection function from the original feature to the latent space. The experimental results on real data demonstrate the effectiveness and robustness of semi-MGPPL.INDEX TERMS Gaussian process, multimodal analysis, feature integration, semi-supervised learning, pseudo-label.
This paper presents a method of interest level estimation via multimodal Gaussian process latent variable factorization (mGPLVF). The proposed method estimates user interest levels for contents with high accuracy by using multi-modal features such as contents and users' behavior. Generally, users' behavior includes some noise, and it is difficult to prepare a large amount of data. For dealing with the problem, the proposed method newly derives mGPLVF calculating appropriate latent variables that do not overfit a small amount of training data including noise based on a probabilistic generative model. Furthermore, mGPLVF simultaneously performs not only construction of the robust latent space but also estimation of user interest levels via the latent variables based on an idea inspired by a factorization machine. The consistent framework of latent space construction and interest level estimation leads to the improvement of the final estimation accuracy. Experimental results show the effectiveness of the proposed method.
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