Autoencoder-based Low-Rank Spectral Ensemble Clustering of Biological Data

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 a...

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Published in2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB) pp. 43 - 46
Main Author Berikov, Vladimir
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
autoencoder
Bioinformatics
Cardiography
cardiotocography
Clustering algorithms
ensemble clustering
low-rank decomposition
Matrix decomposition
Mice
mice protein expression
Partitioning algorithms
Proteins
spectral clustering
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DOI10.1109/CSGB51356.2020.9214622

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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.
AbstractList 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.
Author Berikov, Vladimir
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Snippet This work presents a cluster ensemble algorithm using a combination of low-rank co-association matrix decomposition, deep autoencoder transformation, and...
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StartPage 43
SubjectTerms autoencoder
Bioinformatics
Cardiography
cardiotocography
Clustering algorithms
ensemble clustering
low-rank decomposition
Matrix decomposition
Mice
mice protein expression
Partitioning algorithms
Proteins
spectral clustering
Title Autoencoder-based Low-Rank Spectral Ensemble Clustering of Biological Data
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