Adaptive Fuzzy C-Means with Graph Embedding

• URL: http://arxiv.org/abs/2405.13427v1
• Date: Wed, 22 May 2024 08:15:50 GMT
• Title: Adaptive Fuzzy C-Means with Graph Embedding
• Authors: Qiang Chen, Weizhong Yu, Feiping Nie, Xuelong Li,
• Abstract summary: Fuzzy clustering algorithms can be roughly categorized into two main groups: Fuzzy C-Means (FCM) based methods and mixture model based methods. We propose a novel FCM based clustering model that is capable of automatically learning an appropriate membership degree hyper- parameter value.
• Score: 84.47075244116782
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fuzzy clustering algorithms can be roughly categorized into two main groups: Fuzzy C-Means (FCM) based methods and mixture model based methods. However, for almost all existing FCM based methods, how to automatically selecting proper membership degree hyper-parameter values remains a challenging and unsolved problem. Mixture model based methods, while circumventing the difficulty of manually adjusting membership degree hyper-parameters inherent in FCM based methods, often have a preference for specific distributions, such as the Gaussian distribution. In this paper, we propose a novel FCM based clustering model that is capable of automatically learning an appropriate membership degree hyper-parameter value and handling data with non-Gaussian clusters. Moreover, by removing the graph embedding regularization, the proposed FCM model can degenerate into the simplified generalized Gaussian mixture model. Therefore, the proposed FCM model can be also seen as the generalized Gaussian mixture model with graph embedding. Extensive experiments are conducted on both synthetic and real-world datasets to demonstrate the effectiveness of the proposed model.

Related papers

• Clustering based on Mixtures of Sparse Gaussian Processes [6.939768185086753]
  How to cluster data using their low dimensional embedded space is still a challenging problem in machine learning. In this article, we focus on proposing a joint formulation for both clustering and dimensionality reduction. Our algorithm is based on a mixture of sparse Gaussian processes, which is called Sparse Gaussian Process Mixture Clustering (SGP-MIC)
  arXiv  Detail & Related papers  (2023-03-23T20:44:36Z)
• Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models [19.10047652180224]
  We present a continuous probabilistic modeling methodology for spatial point cloud data using finite Gaussian Mixture Models (GMMs) We use a self-organizing principle from information-theoretic learning to automatically adapt the complexity of the GMM model based on the relevant information in the sensor data. The approach is evaluated against existing point cloud modeling techniques on real-world data with varying degrees of scene complexity.
  arXiv  Detail & Related papers  (2023-01-31T19:28:00Z)
• Hierarchical mixtures of Gaussians for combined dimensionality reduction and clustering [5.819751855626331]
  We show how a family of such two-stage models can be combined into a single, hierarchical model that we call a hierarchical mixture of Gaussians (HMoG) An HMoG simultaneously captures both dimensionality-reduction and clustering, and its performance is quantified in closed-form by the likelihood function. We apply HMoGs to synthetic data and RNA sequencing data, and demonstrate how they exceed the limitations of two-stage models.
  arXiv  Detail & Related papers  (2022-06-10T02:03:18Z)
• Consistency of regularized spectral clustering in degree-corrected mixed membership model [1.0965065178451106]
  We propose an efficient approach called mixed regularized spectral clustering (Mixed-RSC for short) based on the regularized Laplacian matrix. Mixed-RSC is designed based on an ideal cone structure of the variant for the eigen-decomposition of the population regularized Laplacian matrix. We show that the algorithm is consistent under mild conditions by providing error bounds for the inferred membership vector of each node.
  arXiv  Detail & Related papers  (2020-11-23T02:30:53Z)
• Autoregressive Score Matching [113.4502004812927]
  We propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariable log-conditionals (scores) For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
  arXiv  Detail & Related papers  (2020-10-24T07:01:24Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Identification of Probability weighted ARX models with arbitrary domains [75.91002178647165]
  PieceWise Affine models guarantees universal approximation, local linearity and equivalence to other classes of hybrid system. In this work, we focus on the identification of PieceWise Auto Regressive with eXogenous input models with arbitrary regions (NPWARX) The architecture is conceived following the Mixture of Expert concept, developed within the machine learning field.
  arXiv  Detail & Related papers  (2020-09-29T12:50:33Z)
• A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models [78.6363825307044]
  This work presents a mathematical treatment of the relation between Self-Organizing Maps (SOMs) and Gaussian Mixture Models (GMMs) We show that energy-based SOM models can be interpreted as performing gradient descent. This link allows to treat SOMs as generative probabilistic models, giving a formal justification for using SOMs to detect outliers, or for sampling.
  arXiv  Detail & Related papers  (2020-09-24T14:09:04Z)
• Semi-Supervised Learning with Normalizing Flows [54.376602201489995]
  FlowGMM is an end-to-end approach to generative semi supervised learning with normalizing flows. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data.
  arXiv  Detail & Related papers  (2019-12-30T17:36:33Z)

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.