Dynamic Clustering Algorithms via Small-Variance Analysis of Markov Chain Mixture Models

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-variance asymptotics. The analysis yields a scalable online sequence clustering (SOSC) algorithm that is non-parametric in the number of clusters and the subspace dimension of each cluster. SOSC groups the new datapoint in low-dimensional subspaces by online inference in a non-parametric mixture of probabilistic principal component analyzers (MPPCA) based on a Dirichlet process, and captures the state transition and state duration information online in a hidden semi-Markov model (HSMM) based on a hierarchical Dirichlet process. A task-parameterized formulation of our approach autonomously adapts the model to changing environmental situations during manipulation. We apply the algorithm in a teleoperation setting to recognize the intention of the operator and remotely adjust the movement of the robot using the learned model. The generative model is used to synthesize both time-independent and time-dependent behaviors by relying on the principles of shared and autonomous control. Experiments with the Baxter robot yield parsimonious clusters that adapt online with new demonstrations and assist the operator in performing remote manipulation tasks.

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Video Image Segmentation Using Gaussian Mixture Models Based on the Differential Evolution-Based Parameter Estimation

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.474-476.442 ◽

2011 ◽

Vol 474-476 ◽

pp. 442-447

Author(s):

Zhi Gao Zeng ◽

Li Xin Ding ◽

Sheng Qiu Yi ◽

San You Zeng ◽

Zi Hua Qiu

Keyword(s):

Image Segmentation ◽

Differential Evolution ◽

Mixture Models ◽

Clustering Algorithms ◽

Gaussian Mixture Models ◽

Expectation Maximization Algorithm ◽

Image Data ◽

Gaussian Mixture ◽

Parameters Estimation ◽

Video Image

In order to improve the accuracy of the image segmentation in video surveillance sequences and to overcome the limits of the traditional clustering algorithms that can not accurately model the image data sets which Contains noise data, the paper presents an automatic and accurate video image segmentation algorithm, according to the spatial properties, which uses the Gaussian mixture models to segment the image. But the expectation-maximization algorithm is very sensitive to initial values, and easy to fall into local optimums, so the paper presents a differential evolution-based parameters estimation for Gaussian mixture models. The experiment result shows that the segmentation accuracy has been improved greatly than by the traditional segmentation algorithms.

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Markov Chain Sampling Methods for Dirichlet Process Mixture Models

Journal of Computational and Graphical Statistics ◽

10.2307/1390653 ◽

2000 ◽

Vol 9 (2) ◽

pp. 249 ◽

Cited By ~ 159

Author(s):

Radford M. Neal

Keyword(s):

Markov Chain ◽

Mixture Models ◽

Dirichlet Process ◽

Sampling Methods ◽

Dirichlet Process Mixture ◽

Markov Chain Sampling ◽

Dirichlet Process Mixture Models

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Cure fraction models using mixture and non-mixture models

Tatra Mountains Mathematical Publications ◽

10.2478/v10127-012-0001-4 ◽

2012 ◽

Vol 51 (1) ◽

pp. 1-9 ◽

Cited By ~ 9

Author(s):

Jorge A. Achcar ◽

Emílio A. Coelho-Barros ◽

Josmar Mazucheli

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Mixture Models ◽

Censored Data ◽

Life Time ◽

Time Data ◽

Data Set ◽

Cure Fraction ◽

The Bayesian Approach

ABSTRACT We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Two models are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a life- time data set.

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Population mixture models and clustering algorithms

Communication in Statistics- Theory and Methods ◽

10.1080/03610927708827502 ◽

1977 ◽

Vol 6 (5) ◽

pp. 417-434 ◽

Cited By ~ 30

Author(s):

Stanley L. Sclove

Keyword(s):

Mixture Models ◽

Clustering Algorithms

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Markov Chain-Like Model for Prediction Service Based on Improved Hierarchical Particle Swarm Optimization Cluster Algorithm

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194016400064 ◽

2016 ◽

Vol 26 (04) ◽

pp. 653-674 ◽

Cited By ~ 4

Author(s):

Zhifang Liao ◽

Min Liu ◽

Tianhui Song ◽

Li Kuang ◽

Yan Zhang ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Markov Chain ◽

Clustering Algorithms ◽

Particle Swarm ◽

Heterogeneous Data ◽

Swarm Optimization ◽

Log Data ◽

Web Log ◽

Proposed Model ◽

The Web

Since web pages visited by users contain a variety of data resources and the clustering algorithms frequently used for web data do not take the heterogeneous nature into account when processing the heterogeneous data, this paper proposes a new algorithm, namely IHPSOC algorithm, to cluster web log data on the basis of web log mining. Based on particle swarm optimization (PSO), IHPSOC algorithm clusters the web log data through particle swarm iteration. Based on clustering results, this paper establishes Markov chain-like models which create a corresponding Markov chain for users in each different category so as to predict the web resources in users’ need. The results of the experiments show that the proposed model gives better predication.

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