Koshi Watanabe, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama

A Gaussian Process Decoder with Spectral Mixtures and a Locally Estimated Manifold for Data Visualization

  • Fluid Flow and Transfer Processes
  • Computer Science Applications
  • Process Chemistry and Technology
  • General Engineering
  • Instrumentation
  • General Materials Science
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Dimensionality reduction plays an important role in interpreting and visualizing high-dimensional data. Previous methods for data visualization overestimate the local structure and lack the consideration of global preservation. In this study, we develop a Gaussian process latent variable model (GP-LVM) for data visualization. GP-LVMs are one of the frameworks of principal component analysis and preserve the global structure effectively. The drawbacks of GP-LVMs are the absence of local structure preservation and the use of low-expressive kernel functions. Therefore, we introduce regularization for local preservation and an expressive kernel function into GP-LVMs to overcome these limitations. As a result, we reflect the global and local structures in low-dimensional representations, improving the reliability and visibility of embeddings. We conduct qualitative and quantitative experiments comparing baselines and state-of-the-art methods on image and text datasets.

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