期刊
IEEE ACCESS
卷 11, 期 -, 页码 31530-31540出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3257427
关键词
Data visualization; Manifolds; Principal component analysis; Dimensionality reduction; Distance measurement; Oscillators; Distributed databases; dimensionality reduction; spectral-based filtering
This study presents a novel method for effective data visualization by introducing a dual approximation of a data manifold, which overcomes the limitation of previous methods in dealing with locally oscillating data manifolds. The proposed method achieves robust visualization and explicit global preservation.
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.
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