Autoencoder-based Low-Rank Spectral Ensemble Clustering of Biological Data Full article
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Cognitive Sciences, Genomics and Bioinformatics 06-10 Jul 2020 , Новосибирск |
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| Source | Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020 Compilation, IEEE. 2020. 300 c. |
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| Output data | Year: 2020, Article number : 9214622, Pages count : 4 DOI: 10.1109/CSGB51356.2020.9214622 | ||
| Tags | autoencoder; cardiotocography; ensemble clustering; low-rank decomposition; mice protein expression; spectral clustering | ||
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Abstract:
This work presents a cluster ensemble algorithm using a combination of low-rank co-association matrix decomposition, deep autoencoder transformation, and spectral clustering. The suggested algorithm is studied on Mice Protein Expression dataset and Cardiotocography dataset. Monte-Carlo simulations are used to evaluate the clustering performance. The experiments show that the proposed algorithm significantly outperforms other considered variants of clustering framework with respect to clustering accuracy. © 2020 IEEE.
Cite:
Berikov V.
Autoencoder-based Low-Rank Spectral Ensemble Clustering of Biological Data
In compilation Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020. – IEEE., 2020. – C.43-46. DOI: 10.1109/CSGB51356.2020.9214622 Scopus OpenAlex
Autoencoder-based Low-Rank Spectral Ensemble Clustering of Biological Data
In compilation Proceedings - 2020 Cognitive Sciences, Genomics and Bioinformatics, CSGB 2020. – IEEE., 2020. – C.43-46. DOI: 10.1109/CSGB51356.2020.9214622 Scopus OpenAlex
Identifiers:
| Scopus: | 2-s2.0-85094842125 |
| OpenAlex: | W3092551038 |