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Feature Learning for Dimensionality Reduction toward Maximal Extraction of Hidden Patterns
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
University of California, Davis.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-9466-9826
University of California, Davis.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Dimensionality reduction (DR) plays a vital role in the visual analysis of high-dimensional data. One main aim of DR is to reveal hidden patterns that lie on intrinsic low-dimensional manifolds. However, DR often overlooks important patterns when the manifolds are strongly distorted or hidden by certain influential data attributes. This paper presents a feature learning framework, FEALM, designed to generate an optimized set of data projections for nonlinear DR in order to capture important patterns in the hidden manifolds. These projections produce maximally different nearest-neighbor graphs so that resultant DR outcomes are significantly different. To achieve such a capability, we design an optimization algorithm as well as introduce a new graph dissimilarity measure, called neighbor-shape dissimilarity. Additionally, we develop interactive visualizations to assist comparison of obtained DR results and interpretation of each DR result. We demonstrate FEALM's effectiveness through experiments using synthetic datasets and multiple case studies on real-world datasets.

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Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-187949DOI: 10.48550/arXiv.2206.13891OAI: oai:DiVA.org:liu-187949DiVA, id: diva2:1692051
Available from: 2022-08-31 Created: 2022-08-31 Last updated: 2022-09-07Bibliographically approved

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Publisher's full texthttps://arxiv.org/abs/2206.13891

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Fujiwara, TakanoriYnnerman, Anders

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  • apa
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
  • rtf