MPCC: Matching Priors and Conditionals for Clustering

Astorga, Nicolás; Huijse, P.; Protopapas, Pavlos; Estévez, P. A.

doi:10.1007/978-3-030-58592-1_39

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Afterward, a learnable clustering approach was employed to group the data. Astorga et al [28] used GAN for learning from data and applied Matching Priors and Conditionals to cluster data. Zhao et al [29] disentangled the data representation into category part and style part by using augmentation-invariant loss.…”

Section: B Deep Learning Approachesmentioning

confidence: 99%

Automatic Deep Sparse Multi-Trial Vector-based Differential Evolution Clustering with Manifold Learning and Incremental Technique

Hadikhani¹,

Lai²,

Ong³

et al. 2023

Preprint

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<p>Most deep clustering methods despite providing complex networks to learn better from data, use a shallow clustering method. These methods have difficulty in finding good clusters due to the lack of ability to handle between local search and global search to prevent premature convergence. In other words, they do not consider different aspects of the search and it causes them to get stuck in the local optimum. In addition, the majority of existing deep clustering approaches perform clustering with the knowledge of the number of clusters, which is not practical in most real scenarios where such information is not available. To address these problems, this paper presents a novel automatic deep sparse clustering approach based on an evolutionary algorithm called Multi-Trial Vectorbased Differential Evolution (MTDE). Sparse auto-encoder is first applied to extract embedded features. Manifold learning is then adopted to obtain representation and extract the spatial structure of features. Afterward, MTDE clustering is performed without prior information on the number of clusters to find the optimal clustering solution. The proposed approach was evaluated on various datasets, including images and time-series. The results demonstrate that the proposed method improved MTDE by 18.94% on average and compared to the most recent deep clustering algorithms, is consistently among the top three in the majority of datasets. Source code is available on Github: https://github.com/parhamhadikhani/ADSMTDE_Clustering. </p>

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Section: B Deep Learning Approachesmentioning

confidence: 99%

Automatic Deep Sparse Multi-Trial Vector-based Differential Evolution Clustering with Manifold Learning and Incremental Technique

Hadikhani¹,

Lai²,

Ong³

et al. 2023

Preprint

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“…The Matching Priors and Conditionals for Clustering (MPCC) is a GAN-based model featuring an encoder for inferring latent variables and cluster categories from data and a flexible decoder for generating samples from a conditional latent space, according to the researchers of [25]. They show via MPCC that a deep generative model may compete/outperform discriminative approaches in clustering tasks, outperforming the state of the art across a variety of benchmark datasets (MNIST, CIFAR10).…”

Section: Contributions Of Deep Clusteringmentioning

confidence: 99%

Unsupervised Deep Learning: Taxonomy and algorithms

Chefrour

Souici-Meslati

2022

IJCAI

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Clustering is a fundamental challenge in many data-driven application fields and machine learning techniques. The data distribution determines the quality of the outcomes, which has a significant impact on clustering performance. As a result, deep neural networks can be used to learn more accurate data representations for clustering. Many recent studies have focused on employing deep neural networks to develop a clustering-friendly representation, which has resulted in a significant improvement in clustering performance. We present a systematic survey of clustering with deep learning in this study. Then, a taxonomy of deep clustering is proposed, as well as some sample algorithms for our overview. Finally, we discuss some exciting future possibilities for clustering using deep learning and offer some remarks. Povzetek: Ta članek opisuje metode globokega združevanja v skupine in predlaga taksonomijo globokega združevanja v skupine.

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MPCC: Matching Priors and Conditionals for Clustering

Cited by 2 publications

References 17 publications

Automatic Deep Sparse Multi-Trial Vector-based Differential Evolution Clustering with Manifold Learning and Incremental Technique

Automatic Deep Sparse Multi-Trial Vector-based Differential Evolution Clustering with Manifold Learning and Incremental Technique

Unsupervised Deep Learning: Taxonomy and algorithms

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