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.
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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