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
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DOI：
10.1109/icip46576.2022.9898070
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Keisuke Maeda;Takahiro Ogawa;M. Haseyama
中科院分区：
文献类型：
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作者：
Keisuke Maeda;Takahiro Ogawa;M. Haseyama
本文提出，高斯分布式图约束多模式的高斯流程潜在变量模型，用于序数标记的数据。由于用户的歧义是为了捕获包括评分数据在内的多模式数据之间的关系，因此需要考虑不确定性。因此，通过将高斯分布应用于评级数据，我们计算出隐式包括不确定性的分布式标签，因此，可以通过基于高斯laplacian的约束来构建基于其相似性的高斯分布图。分布式图到多模式高斯过程潜在变量模型的目标函数，我们可以实现有效的潜在空间，可以考虑使用标签在不确定性的情况下，这是本文的贡献。
This paper proposes a Gaussian distributed graph constrained multi-modal 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 multi-modal 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.