Unsupervised learning of broad phonetic classes with a statistical mixture model

2004 ◽  
Vol 115 (5) ◽  
pp. 2427-2427
Author(s):  
Ying Lin
Keyword(s):  
Unsupervised Learning ◽  
Mixture Model ◽  
Statistical Mixture ◽  
Statistical Mixture Model

scholarly journals Automatic and Probabilistic Foehn Diagnosis with a Statistical Mixture Model

2014 ◽  
Vol 53 (3) ◽  
pp. 652-659 ◽  
Author(s):  
David Plavcan ◽  
Georg J. Mayr ◽  
Achim Zeileis
Keyword(s):  
Mixture Model ◽  
Potential Temperature ◽  
Statistical Mixture ◽  
Downslope Winds ◽  
Measured Time ◽  
Objective Classification ◽  
One Year ◽  
Expert Classification ◽  
Statistical Mixture Model

AbstractDiagnosing foehn winds from weather station data downwind of topographic obstacles requires distinguishing them from other downslope winds, particularly nocturnal ones driven by radiative cooling. An automatic classification scheme to obtain reproducible results that include information about the (un)certainty of the diagnosis is presented. A statistical mixture model separates foehn and no-foehn winds in a measured time series of wind. In addition to wind speed and direction, it accommodates other physically meaningful classifiers such as the (potential) temperature difference to an upwind station (e.g., near the crest) or relative humidity. The algorithm was tested for Wipp Valley in the central Alps against human expert classification and a previous objective method (Drechsel and Mayr 2008), which the new method outperforms. Climatologically, using only wind information gives nearly identical foehn frequencies as when using additional covariables. A data record length of at least one year is required for satisfactory results. The suitability of mixture models for objective classification of foehn at other locations will have to be tested in further studies.

scholarly journals 2-Way k-Means as a Model for Microbiome Samples

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Weston J. Jackson ◽  
Ipsita Agarwal ◽  
Itsik Pe’er
Keyword(s):  
Unsupervised Learning ◽  
16S Rdna ◽  
Mixture Model ◽  
Vaginal Microbiome ◽  
16S Rdna Sequencing ◽  
Sequencing Data ◽  
Cluster Assignment ◽  
Rdna Sequencing

Motivation. Microbiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones and which therefore lie in between them in the cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project.

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