Spectral clustering via ensemble deep autoencoder learning (SC-EDAE)

•We propose a robust and efficient deep clustering approach with no pre-training.•We combine spectral clustering, deep embeddings and ensemble paradigm strengths.•Our original clustering method inherits the low complexity of landmarks strategy.•The effectiveness is shown through extensive experiment...

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Vydané v:Pattern recognition Ročník 108; s. 107522
Hlavní autori: Affeldt, Séverine, Labiod, Lazhar, Nadif, Mohamed
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.12.2020
Elsevier
Predmet:
Autoencoder
Computer Science
Machine Learning
Spectral clustering
Unsupervised ensemble learning
Unsupervised ensemble learning
Autoencoder
Spectral clustering
spectral clustering
unsupervised ensemble learning
autoencoder
ISSN:0031-3203, 1873-5142
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Popis
Shrnutí:•We propose a robust and efficient deep clustering approach with no pre-training.•We combine spectral clustering, deep embeddings and ensemble paradigm strengths.•Our original clustering method inherits the low complexity of landmarks strategy.•The effectiveness is shown through extensive experiments on real-world datasets. [Display omitted] Several works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These strategies generally improve clustering performance, however deep autoencoder setting issues impede the robustness of these approaches. To alleviate the impact of hyperparameters setting, we propose a model which combines spectral clustering and deep autoencoder strengths in an ensemble framework. Our proposal does not require any pretraining and includes the three following steps: generating various deep embeddings from the original data, constructing a sparse and low-dimensional ensemble affinity matrix based on anchors strategy and applying spectral clustering to obtain the common space shared by multiple deep representations. While the anchors strategy ensures an efficient merging of the encodings, the fusion of various deep representations enables to mitigate the deep networks setting issues. Experiments on various benchmark datasets demonstrate the potential and robustness of our approach compared to state-of-the-art deep clustering methods.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2020.107522