Semi-Supervised Clustering with Neural Networks

Shukla, Ankita; Cheema, Gullal S.; Anand, Sandeep

doi:10.1109/bigmm50055.2020.00030

Cited by 14 publications

(5 citation statements)

References 15 publications

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“…Ren et al 22 proposed a semi-supervised deep embedding clustering model. Shukla et al 23 designed a ClusterNet model to promote the clustering effect through paired semantic constraints. Caron et al 24 proposed a clustering method, DeepCluster, which combines the two tasks of clustering and classification.…”

Section: Related Workmentioning

confidence: 99%

Weakly supervised label propagation algorithm classifies lung cancer imaging subtypes

Ren

Jia

Zhao

et al. 2023

Sci Rep

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Aiming at the problems of long time, high cost, invasive sampling damage, and easy emergence of drug resistance in lung cancer gene detection, a reliable and non-invasive prognostic method is proposed. Under the guidance of weakly supervised learning, deep metric learning and graph clustering methods are used to learn higher-level abstract features in CT imaging features. The unlabeled data is dynamically updated through the k-nearest label update strategy, and the unlabeled data is transformed into weak label data and continue to update the process of strong label data to optimize the clustering results and establish a classification model for predicting new subtypes of lung cancer imaging. Five imaging subtypes are confirmed on the lung cancer dataset containing CT, clinical and genetic information downloaded from the TCIA lung cancer database. The successful establishment of the new model has a significant accuracy rate for subtype classification (ACC = 0.9793), and the use of CT sequence images, gene expression, DNA methylation and gene mutation data from the cooperative hospital in Shanxi Province proves the biomedical value of this method. The proposed method also can comprehensively evaluate intratumoral heterogeneity based on the correlation between the final lung CT imaging features and specific molecular subtypes.

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Section: Related Workmentioning

confidence: 99%

Weakly supervised label propagation algorithm classifies lung cancer imaging subtypes

Ren

Jia

Zhao

et al. 2023

Sci Rep

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“…ClusterNet [50] also uses pairwise semantic constraints from very few labeled data samples (<5% of total data) and exploits the abundant unlabeled data to drive the clustering. The network is optimized by minimizing a combination of k-means-based clustering loss and pairwise KL-divergence loss where the two are regularized via an autoencoder's reconstruction loss and each are defined for both the labeled as well as unlabeled data.…”

Section: Related Workmentioning

confidence: 99%

Soft Semi-Supervised Deep Learning-Based Clustering

AlZuhair,

Ben Ismail,

Bchir

2023

Applied Sciences

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Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learning process towards the optimal data partition and to prevent falling into local minima. However, researchers’ efforts made to improve existing semi-supervised clustering approaches are relatively scarce compared to the contributions made to enhance the state-of-the-art fully unsupervised clustering approaches. In this paper, we propose a novel semi-supervised deep clustering approach, named Soft Constrained Deep Clustering (SC-DEC), that aims to address the limitations exhibited by existing semi-supervised clustering approaches. Specifically, the proposed approach leverages a deep neural network architecture and generates fuzzy membership degrees that better reflect the true partition of the data. In particular, the proposed approach uses side-information and formulates it as a set of soft pairwise constraints to supervise the machine learning process. This supervision information is expressed using rather relaxed constraints named “should-link” constraints. Such constraints determine whether the pairs of data instances should be assigned to the same or different cluster(s). In fact, the clustering task was formulated as an optimization problem via the minimization of a novel objective function. Moreover, the proposed approach’s performance was assessed via extensive experiments using benchmark datasets. Furthermore, the proposed approach was compared to relevant state-of-the-art clustering algorithms, and the obtained results demonstrate the impact of using minimal previous knowledge about the data in improving the overall clustering performance.

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“…Among them, SDEC, (Ren et al, 2019) includes a distance loss function that forces the data points with a must-link to be close in the latent space and vice-versa. Constrained IDEC (Zhang et al, 2019), uses a KL divergence loss instead, extending the work of Shukla et al (2018). Smieja et al (2020) focuses on discriminative clustering methods by self-generating pairwise constraints from Siamese networks.…”

Section: Related Workmentioning

confidence: 99%

Deep Conditional Gaussian Mixture Model for Constrained Clustering

Manduchi¹,

Chin-Cheong²,

Michel³

et al. 2021

Preprint

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Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data. Following recent advances in deep generative models, we propose a novel framework for constrained clustering that is intuitive, interpretable, and can be trained efficiently in the framework of stochastic gradient variational inference. By explicitly integrating domain knowledge in the form of probabilistic relations, our proposed model (DC-GMM) uncovers the underlying distribution of data conditioned on prior clustering preferences, expressed as pairwise constraints. These constraints guide the clustering process towards a desirable partition of the data by indicating which samples should or should not belong to the same cluster. We provide extensive experiments to demonstrate that DC-GMM shows superior clustering performances and robustness compared to state-of-the-art deep constrained clustering methods on a wide range of data sets. We further demonstrate the usefulness of our approach on two challenging real-world applications.Preprint. Under review.

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Semi-Supervised Clustering with Neural Networks

Cited by 14 publications

References 15 publications

Weakly supervised label propagation algorithm classifies lung cancer imaging subtypes

Weakly supervised label propagation algorithm classifies lung cancer imaging subtypes

Soft Semi-Supervised Deep Learning-Based Clustering

Deep Conditional Gaussian Mixture Model for Constrained Clustering

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