Tensor-Based Unsupervised Multi-View Feature Selection for Image Recognition Article Swipe
Yongshan Zhang
,
Xinxin Wang
,
Zhihua Cai
,
Yicong Zhou
,
Philip S. Yu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/icme51207.2021.9428428
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/icme51207.2021.9428428
In image analysis, image samples from multiple sources may contain noisy features. Due to the difficulty of obtaining label information and complex intrinsic structures, performing unsupervised feature selection on multi-view data is a challenging problem. Most existing unsupervised multi-view feature selection methods may explore only the inter-view correlations at the view-level, and ignore the explicit correlations between features across multiple views. In this paper, we propose a tensor-based unsupervised multi-view feature selection (TUFS) method. Specifically, TUFS efficiently explores the full-order interactions among multi-view data without physically building a tensor. Besides, multiple local geometric structures for different views are constructed to facilitate unsupervised feature selection. To solve the proposed model, we design an alternating optimization algorithm. Experiments and comparisons on three image datasets demonstrate that the proposed TUFS yields better performance over the state-of-the-art methods.
Related Topics
Concepts
Computer science
Feature selection
Artificial intelligence
Pattern recognition (psychology)
Tensor (intrinsic definition)
Selection (genetic algorithm)
Feature (linguistics)
Image (mathematics)
Feature extraction
Unsupervised learning
Machine learning
Data mining
Mathematics
Linguistics
Pure mathematics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/icme51207.2021.9428428
- OA Status
- gold
- Cited By
- 8
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3168790186
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3168790186Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/icme51207.2021.9428428Digital Object Identifier
- Title
-
Tensor-Based Unsupervised Multi-View Feature Selection for Image RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-06-09Full publication date if available
- Authors
-
Yongshan Zhang, Xinxin Wang, Zhihua Cai, Yicong Zhou, Philip S. YuList of authors in order
- Landing page
-
https://doi.org/10.1109/icme51207.2021.9428428Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/icme51207.2021.9428428Direct OA link when available
- Concepts
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Computer science, Feature selection, Artificial intelligence, Pattern recognition (psychology), Tensor (intrinsic definition), Selection (genetic algorithm), Feature (linguistics), Image (mathematics), Feature extraction, Unsupervised learning, Machine learning, Data mining, Mathematics, Linguistics, Pure mathematics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 2, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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21Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2024165284, https://openalex.org/W2753823607, https://openalex.org/W2084376194, https://openalex.org/W2583200575, https://openalex.org/W3000675402, https://openalex.org/W2896042878, https://openalex.org/W6731811539, https://openalex.org/W2979744929, https://openalex.org/W6735994599, https://openalex.org/W2128873747, https://openalex.org/W6681822384, https://openalex.org/W2904125861, https://openalex.org/W2583916359, https://openalex.org/W2407812036, https://openalex.org/W2946807672, https://openalex.org/W1937059634, https://openalex.org/W6773235835, https://openalex.org/W2604723872, https://openalex.org/W2579597427, https://openalex.org/W3004244740, https://openalex.org/W2149620660 |
| referenced_works_count | 21 |
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| sustainable_development_goals[0].display_name | Sustainable cities and communities |
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| citation_normalized_percentile.is_in_top_10_percent | False |