Online Arbitrary Shaped Clustering through Correlated Gaussian Functions

• URL: http://arxiv.org/abs/2302.06335v2
• Date: Mon, 20 Nov 2023 08:03:40 GMT
• Title: Online Arbitrary Shaped Clustering through Correlated Gaussian Functions
• Authors: Ole Christian Eidheim
• Abstract summary: A novel online clustering algorithm is presented that can produce arbitrary shaped clusters from inputs in an unsupervised manner. The algorithm can be deemed more biologically plausible than model optimization through backpropagation, although practical applicability may require additional research.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is no convincing evidence that backpropagation is a biologically plausible mechanism, and further studies of alternative learning methods are needed. A novel online clustering algorithm is presented that can produce arbitrary shaped clusters from inputs in an unsupervised manner, and requires no prior knowledge of the number of clusters in the input data. This is achieved by finding correlated outputs from functions that capture commonly occurring input patterns. The algorithm can be deemed more biologically plausible than model optimization through backpropagation, although practical applicability may require additional research. However, the method yields satisfactory results on several toy datasets on a noteworthy range of hyperparameters.

Related papers

• Gram-Schmidt Methods for Unsupervised Feature Extraction and Selection [7.373617024876725]
  We propose a Gram-Schmidt process over function spaces to detect and map out nonlinear dependencies. We provide experimental results for synthetic and real-world benchmark datasets. Surprisingly, our linear feature extraction algorithms are comparable and often outperform several important nonlinear feature extraction methods.
  arXiv  Detail & Related papers  (2023-11-15T21:29:57Z)
• Causal Feature Selection via Transfer Entropy [59.999594949050596]
  Causal discovery aims to identify causal relationships between features with observational data. We introduce a new causal feature selection approach that relies on the forward and backward feature selection procedures. We provide theoretical guarantees on the regression and classification errors for both the exact and the finite-sample cases.
  arXiv  Detail & Related papers  (2023-10-17T08:04:45Z)
• Efficient Model-Free Exploration in Low-Rank MDPs [76.87340323826945]
  Low-Rank Markov Decision Processes offer a simple, yet expressive framework for RL with function approximation. Existing algorithms are either (1) computationally intractable, or (2) reliant upon restrictive statistical assumptions. We propose the first provably sample-efficient algorithm for exploration in Low-Rank MDPs.
  arXiv  Detail & Related papers  (2023-07-08T15:41:48Z)
• Learning to Bound Counterfactual Inference in Structural Causal Models from Observational and Randomised Data [64.96984404868411]
  We derive a likelihood characterisation for the overall data that leads us to extend a previous EM-based algorithm. The new algorithm learns to approximate the (unidentifiability) region of model parameters from such mixed data sources. It delivers interval approximations to counterfactual results, which collapse to points in the identifiable case.
  arXiv  Detail & Related papers  (2022-12-06T12:42:11Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms [82.90843777097606]
  We propose a causally-aware imputation algorithm (MIRACLE) for missing data. MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism. We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation.
  arXiv  Detail & Related papers  (2021-11-04T22:38:18Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Boolean Reasoning-Based Biclustering for Shifting Pattern Extraction [0.20305676256390928]
  Biclustering is a powerful approach to search for patterns in data, as it can be driven by a function that measures the quality of diverse types of patterns of interest. Shifting patterns are specially interesting as they account constant fluctuations in data. This work is presented to show that the induction of shifting patterns by means of Boolean reasoning is due to the ability of finding all inclusion--maximal delta-shifting patterns.
  arXiv  Detail & Related papers  (2021-04-26T11:40:17Z)
• Distributed Learning via Filtered Hyperinterpolation on Manifolds [2.2046162792653017]
  This paper studies the problem of learning real-valued functions on manifold. Motivated by the problem of handling large data sets, it presents a parallel data processing approach. We prove quantitative relations between the approximation quality of the learned function over the entire manifold.
  arXiv  Detail & Related papers  (2020-07-18T10:05:18Z)
• Flexible Bayesian Nonlinear Model Configuration [10.865434331546126]
  Linear, or simple parametric, models are often not sufficient to describe complex relationships between input variables and a response. We introduce a flexible approach for the construction and selection of highly flexible nonlinear parametric regression models. A genetically modified mode jumping chain Monte Carlo algorithm is adopted to perform Bayesian inference.
  arXiv  Detail & Related papers  (2020-03-05T21:20:55Z)

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.