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

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

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 inte...

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Bibliographic Details
Main Authors: Kyohei Kamikawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Gaussian process
multimodal analysis
feature integration
semi-supervised learning
pseudo-label
Online Access:https://ieeexplore.ieee.org/document/9632599/

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