Feature Integration via Semi-Supervised Ordinally Multi-Modal Gaussian Process Latent Variable Model

Kamikawa, Kyohei; Maeda, Kazuo; Ogawa, Takahiro; Haseyama, Miki

doi:10.1109/icassp39728.2021.9414109

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…mGPLVM is a kind of stochastic generative models assuming that multi-modal observed variables are generated by common latent variables and kernel hyper-parameters that are optimized in the model. Due to its flexible characteristics, mGPLVM series have recently been reported to be more effective in constructing the latent space than deep learningbased methods [8][9][10]. Various extensions such as a method to preserve the topological structure of high-dimensional data [11] and a method to share the prior distribution among modalities [12] have been developed.…”

Section: Introductionmentioning

confidence: 99%

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

Maeda

Ogawa

Haseyama

2022

2022 IEEE International Conference on Image Processing (ICIP)

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This paper proposes a Gaussian distributed graph constrained multimodal Gaussian process latent variable model for ordinal labeled data. Rating data that are used in various real-world applications such as product recommendation can represent user preferences, but the difference between adjacent ratings is often uncertain due to the user's ambiguity. In order to capture the relationships among multimodal data including rating data, consideration of the uncertainty is necessary. Therefore, by applying the Gaussian distribution to the rating data, we calculate distributed labels that implicitly include the uncertainty, and thus, the Gaussian distributed graph based on their similarities can be constructed. By introducing a constraint calculated based on the graph Laplacian of the Gaussian distributed graph into the objective function of the multi-modal Gaussian process latent variable model, we can achieve an effective latent space that can consider a label correlation while accounting for the uncertainty. This is the contribution of this paper. The effectiveness of the proposed method is verified by experiments using some open datasets.

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Section: Introductionmentioning

confidence: 99%

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

Maeda

Ogawa

Haseyama

2022

2022 IEEE International Conference on Image Processing (ICIP)

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“…In studies using wearable sensors, healthcare monitoring systems using vital data, such as blood pressure, heart rate, weight, and blood glucose, have been proposed [ 17 , 18 ]. Additionally, several studies estimated the interests of content involving human behavior [ 19 , 20 ] and personalized saliency (and its prediction) using gaze data [ 21 , 22 , 23 ]. Furthermore, the analysis of behavior, gaze data, and brain activity contribute to the solutions to several tasks, such as brain decoding [ 24 , 25 , 26 , 27 ] and certain applications [ 28 , 29 , 30 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

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

Maeda

Togo

Ogawa

et al. 2022

Sensors

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This paper presents a trial analysis of the relationship between taste and biological information obtained while eating strawberries (for a sensory evaluation). This study used the visual analog scale (VAS); we collected questionnaires used in previous studies and human brain activity obtained while eating strawberries. In our analysis, we assumed that brain activity is highly correlated with taste. Then, the relationships between brain activity and other data, such as VAS and questionnaires, could be analyzed through a canonical correlation analysis, which is a multivariate analysis. Through an analysis of brain activity, the potential relationship with "taste" (that is not revealed by the initial simple correlation analysis) can be discovered. This is the main contribution of this study. In the experiments, we discovered the potential relationship between cultural factors (in the questionnaires) and taste. We also found a strong relationship between taste and individual information. In particular, the analysis of cross-loading between brain activity and individual information suggests that acidity and the sugar-to-acid ratio are related to taste.

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Feature Integration Through Semi-Supervised Multimodal Gaussian Process Latent Variable Model With Pseudo-Labels for Interest Level Estimation

et al. 2021

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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.

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Feature Integration via Semi-Supervised Ordinally Multi-Modal Gaussian Process Latent Variable Model

Cited by 4 publications

References 25 publications

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

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

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

Feature Integration Through Semi-Supervised Multimodal Gaussian Process Latent Variable Model With Pseudo-Labels for Interest Level Estimation

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