Multi-View Spectral Clustering via ELM-AE Ensemble Features Representations Learning Article Swipe
Lijuan Wang
,
Shifei Ding
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3034624
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3034624
Spectral cluster based on multi-view data has proven effective for clustering multi-source real-world data because consensus and complementary information of multi-view data ensure the result of clustering. Feature learning is the vital step in spectral clustering, and excellent feature representations can effectively improve spectral clustering performance. In this article, we propose Multi-View Spectral Clustering via ELM-Autoencoder Ensemble Features Representations Learning (MvSC-EF-ELM). First, Extreme Learning Machine as an Autoencoder(ELM-AE) learns feature representations by adopting a singular value decomposition, which reconstructs the inputs using the embedding feature space. Second, the single view and multi-view spectral clustering algorithm are applied embedding feature representations space into the eigenspace and cluster's them, respectively. Experiments on benchmark datasets demonstrate that our approach empirically validates the power of ELM-AE for feature learning from raw data and may effectively facilitate multi-view spectral clustering and induce superior clustering results.
Related Topics
Concepts
Cluster analysis
Autoencoder
Pattern recognition (psychology)
Artificial intelligence
Spectral clustering
Computer science
Feature (linguistics)
Extreme learning machine
Feature learning
Spectral space
Correlation clustering
Embedding
Feature vector
Benchmark (surveying)
CURE data clustering algorithm
Mathematics
Deep learning
Artificial neural network
Philosophy
Linguistics
Geography
Pure mathematics
Geodesy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.3034624
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09243937.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3097115940
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3097115940Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2020.3034624Digital Object Identifier
- Title
-
Multi-View Spectral Clustering via ELM-AE Ensemble Features Representations LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Lijuan Wang, Shifei DingList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.3034624Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09243937.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09243937.pdfDirect OA link when available
- Concepts
-
Cluster analysis, Autoencoder, Pattern recognition (psychology), Artificial intelligence, Spectral clustering, Computer science, Feature (linguistics), Extreme learning machine, Feature learning, Spectral space, Correlation clustering, Embedding, Feature vector, Benchmark (surveying), CURE data clustering algorithm, Mathematics, Deep learning, Artificial neural network, Philosophy, Linguistics, Geography, Pure mathematics, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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