Distributed Label Dequantized Gaussian Process Latent Variable Model for Multi-View Data Integration

Watanabe, Koshi; Maeda, Kazuo; Ogawa, Takahiro; Haseyama, Miki

doi:10.1109/icassp43922.2022.9747681

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Many state-of-the-art studies have recently attempted to address this issue by introducing better initialization [3], [17], diffusion operation [18], and triplet interaction [19], [20] into graph embedding approaches. The other important problem is the sensitivity to local oscillation [21]. Conventional and recent methods only use the local distance measurement and not other global information, to approximate the data manifold.…”

Section: Introductionmentioning

confidence: 99%

SpectralMAP: Approximating Data Manifold With Spectral Decomposition

et al. 2023

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Dimensionality reduction is widely used to visualize complex high-dimensional data. This study presents a novel method for effective data visualization. Previous methods depend on local distance measurements for data manifold approximation. This leads to unreliable results when a data manifold locally oscillates because of some undesirable effects, such as noise effects. In this study, we overcome this limitation by introducing a dual approximation of a data manifold. We roughly approximate a data manifold with a neighborhood graph and prune it with a global filter. This dual scheme results in local oscillation robustness and yields effective visualization with explicit global preservation. We consider a global filter based on principal component analysis frameworks and derive it with the spectral information of the original high-dimensional data. Finally, we experiment with multiple datasets to verify our method, compare its performance to that of state-of-the-art methods, and confirm the effectiveness of our novelty and results.INDEX TERMS Data visualization, dimensionality reduction, spectral-based filtering.

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Section: Introductionmentioning

confidence: 99%

SpectralMAP: Approximating Data Manifold With Spectral Decomposition

et al. 2023

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“…Although here we will rely on the prototypical GPLVM, several variants thereof exist; see e.g.,[22].…”

mentioning

confidence: 99%

Higher-Order Link Prediction Via Learnable Maximum Mean Discrepancy

Karanikolas

Pagès-Zamora

Giannakis

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Higher-order link prediction (HOLP) seeks missing links capturing dependencies among three or more network nodes. Predicting high-order links (HOLs) can for instance reveal hyperlinks in the structure of drug substance and metabolic networks. Existing methods either make restrictive assumptions regarding the emergence of HOLs, or, they rely on reduced dimensionality models of limited expressiveness. To overcome these limitations, the HOLP approach developed here leverages distribution similarities across embeddings as captured by a learnable probability metric. The intuition underpinning the novel approach is that sets of nodes whose embeddings are less similar in distribution, are less likely to be connected by a HOL. Specifically, nonlinear dimensionality reduction is effected through a Gaussian process latent variable model that yields nodal embeddings, and also learns a data-driven similarity function (kernel). This kernel forms the core of a maximum mean discrepancy probability metric. Tests on benchmark datasets illustrate the potential of the proposed approach.

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Distributed Label Dequantized Gaussian Process Latent Variable Model for Multi-View Data Integration

Cited by 2 publications

References 21 publications

SpectralMAP: Approximating Data Manifold With Spectral Decomposition

SpectralMAP: Approximating Data Manifold With Spectral Decomposition

Higher-Order Link Prediction Via Learnable Maximum Mean Discrepancy

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