Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
Mixture models for learning low-dimensional roles in high-dimensional data
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixed-membership naive Bayes models
Data Mining and Knowledge Discovery
Detection of communities and bridges in weighted networks
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Learning partial ordinal class memberships with kernel-based proportional odds models
Computational Statistics & Data Analysis
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We present a principled Bayesian framework for modeling partial memberships of data points to clusters. Unlike a standard mixture model which assumes that each data point belongs to one and only one mixture component, or cluster, a partial membership model allows data points to have fractional membership in multiple clusters. Algorithms which assign data points partial memberships to clusters can be useful for tasks such as clustering genes based on microarray data (Gasch & Eisen, 2002). Our Bayesian Partial Membership Model (BPM) uses exponential family distributions to model each cluster, and a product of these distibtutions, with weighted parameters, to model each datapoint. Here the weights correspond to the degree to which the datapoint belongs to each cluster. All parameters in the BPM are continuous, so we can use Hybrid Monte Carlo to perform inference and learning. We discuss relationships between the BPM and Latent Dirichlet Allocation, Mixed Membership models, Exponential Family PCA, and fuzzy clustering. Lastly, we show some experimental results and discuss nonparametric extensions to our model.