A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification

• URL: http://arxiv.org/abs/2009.00822v1
• Date: Wed, 2 Sep 2020 05:10:13 GMT
• Title: A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification
• Authors: Vikas Singh, Homanga Bharadhwaj, Nishchal K Verma
• Abstract summary: fuzzyc-regression clustering based on type-2 fuzzyset has been shown the remarkable results on non-sparse data. Aninnovative architecture for fuzzyc-regression model is presented and a novel student-tdistribution based membership functionis designed for sparse data modelling.
• Score: 47.25472624305589
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clustering techniques have been proved highly suc-cessful for Takagi-Sugeno (T-S) fuzzy model identification. Inparticular, fuzzyc-regression clustering based on type-2 fuzzyset has been shown the remarkable results on non-sparse databut their performance degraded on sparse data. In this paper, aninnovative architecture for fuzzyc-regression model is presentedand a novel student-tdistribution based membership functionis designed for sparse data modelling. To avoid the overfitting,we have adopted a Bayesian approach for incorporating aGaussian prior on the regression coefficients. Additional noveltyof our approach lies in type-reduction where the final output iscomputed using Karnik Mendel algorithm and the consequentparameters of the model are optimized using Stochastic GradientDescent method. As detailed experimentation, the result showsthat proposed approach outperforms on standard datasets incomparison of various state-of-the-art methods.

Related papers

• Sparse Tensor PCA via Tensor Decomposition for Unsupervised Feature Selection [8.391109286933856]
  We develop two Sparse Principal Component Analysis (STPCA) models that utilize the projection directions in the factor matrices to perform unsupervised feature selection. For both models, we prove the optimal solution of each subproblem falls onto the Hermitian Positive Semidefinite Cone (HPSD) According to the experimental results, the two proposed methods are suitable for handling different data tensor scenarios and outperform the state-of-the-art UFS methods.
  arXiv  Detail & Related papers  (2024-07-24T04:04:56Z)
• Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders [22.77397537980102]
  We show that we can compute a deterministic, model-agnostic posterior approximation (MAPA) of the true model's posterior. We present preliminary results on low-dimensional synthetic data that (1) MAPA captures the trend of the true posterior, and (2) our MAPA-based inference performs better density estimation with less computation than baselines.
  arXiv  Detail & Related papers  (2024-03-13T20:16:21Z)
• A model-free feature selection technique of feature screening and random forest based recursive feature elimination [0.0]
  We propose a model-free feature selection method for ultra-high dimensional data with mass features. We show that the proposed method is selection consistent and $L$ consistent under weak regularity conditions.
  arXiv  Detail & Related papers  (2023-02-15T03:39:16Z)
• Variational Laplace Autoencoders [53.08170674326728]
  Variational autoencoders employ an amortized inference model to approximate the posterior of latent variables. We present a novel approach that addresses the limited posterior expressiveness of fully-factorized Gaussian assumption. We also present a general framework named Variational Laplace Autoencoders (VLAEs) for training deep generative models.
  arXiv  Detail & Related papers  (2022-11-30T18:59:27Z)
• Score-based Continuous-time Discrete Diffusion Models [102.65769839899315]
  We extend diffusion models to discrete variables by introducing a Markov jump process where the reverse process denoises via a continuous-time Markov chain. We show that an unbiased estimator can be obtained via simple matching the conditional marginal distributions. We demonstrate the effectiveness of the proposed method on a set of synthetic and real-world music and image benchmarks.
  arXiv  Detail & Related papers  (2022-11-30T05:33:29Z)
• Dynamically-Scaled Deep Canonical Correlation Analysis [77.34726150561087]
  Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them. We introduce a novel dynamic scaling method for training an input-dependent canonical correlation model.
  arXiv  Detail & Related papers  (2022-03-23T12:52:49Z)
• Optimizing model-agnostic Random Subspace ensembles [5.680512932725364]
  We present a model-agnostic ensemble approach for supervised learning. The proposed approach alternates between learning an ensemble of models using a parametric version of the Random Subspace approach. We show the good performance of the proposed approach, both in terms of prediction and feature ranking, on simulated and real-world datasets.
  arXiv  Detail & Related papers  (2021-09-07T13:58:23Z)
• Sparsely constrained neural networks for model discovery of PDEs [0.0]
  We present a modular framework that determines the sparsity pattern of a deep-learning based surrogate using any sparse regression technique. We show how a different network architecture and sparsity estimator improve model discovery accuracy and convergence on several benchmark examples.
  arXiv  Detail & Related papers  (2020-11-09T11:02:40Z)
• 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)
• Model Fusion with Kullback--Leibler Divergence [58.20269014662046]
  We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors.
  arXiv  Detail & Related papers  (2020-07-13T03:27:45Z)

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.