Semi-supervised deep embedded clustering

Ren, Yazhou; Hu, Kangrong; Dai, Xinyi; Pan, Lili; Hoi, Steven C. H.; Xu, Zenglin

doi:10.1016/j.neucom.2018.10.016

Cited by 154 publications

(105 citation statements)

References 25 publications

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“…Xie et al [39] propose deep embedded clustering (DEC) to jointly learn the cluster assignment and the feature representations. Ren et al [27] propose semi-supervised deep embedded clustering to enhance the performance of DEC by using pairwise constraints. Yang et al [43] and Chang et al [6] apply convolutional neural networks (CNN) for exploring image clusters.…”

Section: Related Work 21 Deep Clusteringmentioning

confidence: 99%

Deep density-based image clustering

Ren

Wang

et al. 2020

Knowledge-Based Systems

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Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications. However, the existing deep clustering algorithms generally need the number of clusters in advance, which is usually unknown in real-world tasks. In addition, the initial cluster centers in the learned feature space are generated by k-means. This only works well on spherical clusters and probably leads to unstable clustering results. In this paper, we propose a two-stage deep densitybased image clustering (DDC) framework to address these issues. The first stage is to train a deep convolutional autoencoder (CAE) to extract low-dimensional feature representations from high-dimensional image data, and then apply t-SNE to further reduce the data to a 2-dimensional space favoring density-based clustering algorithms. The second stage is to apply the developed density-based clustering technique on the 2-dimensional embedded data to automatically recognize an appropriate number of clusters with arbitrary shapes. Concretely, a number of local clusters are generated to capture the local structures of clusters, and then are merged via their density relationship to form the final clustering result. Experiments demonstrate that the proposed DDC achieves comparable or even better clustering performance than state-of-the-art deep clustering methods, even though the number of clusters is not given.

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Section: Related Work 21 Deep Clusteringmentioning

confidence: 99%

Deep density-based image clustering

Ren

Wang

et al. 2020

Knowledge-Based Systems

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“…Clustering partitions the data objects into different groups based on certain property [18][19][20]. As a classical task, a number of clustering techniques have been developed.…”

Section: Introductionmentioning

confidence: 99%

Robust Graph Learning From Noisy Data

Kang

Pan

Hoi

et al. 2020

IEEE Trans. Cybern.

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241

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Learning graphs from data automatically has shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust PCA, where the quality of the graph plays a critical role. The proposed model is able to boost the performance of data clustering, semisupervised classification, and data recovery significantly, primarily due to two key factors: 1) enhanced low-rank recovery by exploiting the graph smoothness assumption, 2) improved graph construction by exploiting clean data recovered by robust PCA. Thus, it boosts the clustering, semi-supervised classification, and data recovery performance overall. Extensive experiments on image/document clustering, object recognition, image shadow removal, and video background subtraction reveal that our model outperforms the previous state-of-the-art methods.

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“…Discovering clusters in unlabeled data is one of the most fundamental scientific tasks, with an endless list of practical applications in data mining, pattern recognition, and machine learning [1,2,3,4,5]. It is well-known that labels are expensive to obtain, so clustering techniques are useful tools to process data and to reveal its underlying structure.…”

Section: Introductionmentioning

confidence: 99%

Clustering with similarity preserving

Kang

Wang

et al. 2019

Neurocomputing

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Graph-based clustering has shown promising performance in many tasks.A key step of graph-based approach is the similarity graph construction. In general, learning graph in kernel space can enhance clustering accuracy due to the incorporation of nonlinearity. However, most existing kernel-based graph learning mechanisms is not similarity-preserving, hence leads to sub-optimal performance. To overcome this drawback, we propose a more discriminative graph learning method which can preserve the pairwise similarities between samples in an adaptive manner for the first time. Specifically, we require the learned graph be close to a kernel matrix, which serves as a measure of similarity in raw data. Moreover, the structure is adaptively tuned so that the number of connected components of the graph is exactly equal to the number of clusters. Finally, our method unifies clustering and graph learning which can directly obtain cluster indicators from the graph itself without performing further clustering step. The effectiveness of this approach is examined on both single and multiple kernel learning scenarios in several

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Semi-supervised deep embedded clustering

Cited by 154 publications

References 25 publications

Deep density-based image clustering

Deep density-based image clustering

Robust Graph Learning From Noisy Data

Clustering with similarity preserving

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