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A Novel Angle-Based Learning Framework on Semi-supervised Dimensionality Reduction in High-Dimensional Data with Application to Action Recognition
Department of Statistics, University of Mazandaran, Babolsar, Iran.
Department of Statistics, University of Mazandaran, Babolsar, Iran.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-9235-7861
2020 (English)In: Arabian Journal for Science and Engineering, ISSN 2193-567X, Vol. 45, no 12, p. 11051-11063Article in journal (Refereed) Published
Abstract [en]

The existing outliers in high-dimensional data create various challenges to classify datasets such as the exact classification with imbalanced scatters. In this paper, we propose an angle-based framework as Angle Global and Local Discriminant Analysis (AGLDA) to consider imbalanced scatters. AGLDA chooses an optimal subspace by using angle cosine to achieve appropriate scatter balance in the dataset. The privilege of this method is to classify datasets with the effect of outliers by finding optimal subspace in high-dimensional data. Generally, this method is more effective and more reliable than other methods to classify data when there are outliers. Besides, human posture classification has been used as an application of the balanced semi-supervised dimensionality reduction to assist human factor experts and designers of industrial systems for diagnosing the type of maintenance crew postures. The experimental results show the efficiency of the proposed method via two real case studies, and the results have also been verified by comparing it with other approaches.

Place, publisher, year, edition, pages
Springer, 2020. Vol. 45, no 12, p. 11051-11063
Keywords [en]
High-dimensional data, Dimensionality reduction, Human factor, Angle-based discriminant, Scatter balance
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-81043DOI: 10.1007/s13369-020-04869-wISI: 000572302500002Scopus ID: 2-s2.0-85091403486OAI: oai:DiVA.org:ltu-81043DiVA, id: diva2:1473726
Note

Validerad;2020;Nivå 2;2020-12-03 (alebob)

Available from: 2020-10-07 Created: 2020-10-07 Last updated: 2020-12-03Bibliographically approved

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Teymourian, Kiumars

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CiteExportLink to record
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Citation style
  • apa
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