Self-Evolutionary Clustering

• URL: http://arxiv.org/abs/2202.10505v1
• Date: Mon, 21 Feb 2022 19:38:18 GMT
• Title: Self-Evolutionary Clustering
• Authors: Hanxuan Wang, Na Lu, Qinyang Liu
• Abstract summary: Most existing deep clustering methods are based on simple distance comparison and highly dependent on the target distribution generated by a handcrafted nonlinear mapping. A novel modular Self-Evolutionary Clustering (Self-EvoC) framework is constructed, which boosts the clustering performance by classification in a self-supervised manner. The framework can efficiently discriminate sample outliers and generate better target distribution with the assistance of self-supervised.
• Score: 1.662966122370634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep clustering outperforms conventional clustering by mutually promoting representation learning and cluster assignment. However, most existing deep clustering methods suffer from two major drawbacks. First, most cluster assignment methods are based on simple distance comparison and highly dependent on the target distribution generated by a handcrafted nonlinear mapping. These facts largely limit the possible performance that deep clustering methods can reach. Second, the clustering results can be easily guided towards wrong direction by the misassigned samples in each cluster. The existing deep clustering methods are incapable of discriminating such samples. To address these issues, a novel modular Self-Evolutionary Clustering (Self-EvoC) framework is constructed, which boosts the clustering performance by classification in a self-supervised manner. Fuzzy theory is used to score the sample membership with probability which evaluates the intermediate clustering result certainty of each sample. Based on which, the most reliable samples can be selected and augmented. The augmented data are employed to fine-tune an off-the-shelf deep network classifier with the labels from the clustering, which results in a model to generate the target distribution. The proposed framework can efficiently discriminate sample outliers and generate better target distribution with the assistance of self-supervised classifier. Extensive experiments indicate that the Self-EvoC remarkably outperforms state-of-the-art deep clustering methods on three benchmark datasets.

Related papers

• GCC: Generative Calibration Clustering [55.44944397168619]
  We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure. First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
  arXiv  Detail & Related papers  (2024-04-14T01:51:11Z)
• Deep Embedding Clustering Driven by Sample Stability [16.53706617383543]
  We propose a deep embedding clustering algorithm driven by sample stability (DECS) Specifically, we start by constructing the initial feature space with an autoencoder and then learn the cluster-oriented embedding feature constrained by sample stability. The experimental results on five datasets illustrate that the proposed method achieves superior performance compared to state-of-the-art clustering approaches.
  arXiv  Detail & Related papers  (2024-01-29T09:19:49Z)
• Stable Cluster Discrimination for Deep Clustering [7.175082696240088]
  Deep clustering can optimize representations of instances (i.e., representation learning) and explore the inherent data distribution. The coupled objective implies a trivial solution that all instances collapse to the uniform features. In this work, we first show that the prevalent discrimination task in supervised learning is unstable for one-stage clustering. A novel stable cluster discrimination (SeCu) task is proposed and a new hardness-aware clustering criterion can be obtained accordingly.
  arXiv  Detail & Related papers  (2023-11-24T06:43:26Z)
• A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments [54.172993875654015]
  The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments. One-shot approach, based on local computations at the users and a clustering based aggregation step at the server is shown to provide strong learning guarantees. For strongly convex problems it is shown that, as long as the number of data points per user is above a threshold, the proposed approach achieves order-optimal mean-squared error rates in terms of the sample size.
  arXiv  Detail & Related papers  (2022-09-22T09:04:10Z)
• Implicit Sample Extension for Unsupervised Person Re-Identification [97.46045935897608]
  Clustering sometimes mixes different true identities together or splits the same identity into two or more sub clusters. We propose an Implicit Sample Extension (OurWholeMethod) method to generate what we call support samples around the cluster boundaries. Experiments demonstrate that the proposed method is effective and achieves state-of-the-art performance for unsupervised person Re-ID.
  arXiv  Detail & Related papers  (2022-04-14T11:41:48Z)
• Learning Statistical Representation with Joint Deep Embedded Clustering [2.1267423178232407]
  StatDEC is an unsupervised framework for joint statistical representation learning and clustering. Our experiments show that using these representations, one can considerably improve results on imbalanced image clustering across a variety of image datasets.
  arXiv  Detail & Related papers  (2021-09-11T09:26:52Z)
• You Never Cluster Alone [150.94921340034688]
  We extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation. We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one. By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps.
  arXiv  Detail & Related papers  (2021-06-03T14:59:59Z)
• Deep Fair Discriminative Clustering [24.237000220172906]
  We study a general notion of group-level fairness for binary and multi-state protected status variables (PSVs) We propose a refinement learning algorithm to combine the clustering goal with the fairness objective to learn fair clusters adaptively. Our framework shows promising results for novel clustering tasks including flexible fairness constraints, multi-state PSVs and predictive clustering.
  arXiv  Detail & Related papers  (2021-05-28T23:50:48Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)
• Progressive Cluster Purification for Unsupervised Feature Learning [48.87365358296371]
  In unsupervised feature learning, sample specificity based methods ignore the inter-class information. We propose a novel clustering based method, which excludes class inconsistent samples during progressive cluster formation. Our approach, referred to as Progressive Cluster Purification (PCP), implements progressive clustering by gradually reducing the number of clusters during training.
  arXiv  Detail & Related papers  (2020-07-06T08:11:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.