An Introduction to Variational Methods for Graphical Models
Machine Learning
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust mixture modelling using the t distribution
Statistics and Computing
Robust probabilistic projections
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Neural Networks
Robust mixtures in the presence of measurement errors
Proceedings of the 24th international conference on Machine learning
Inferring gene regulatory networks from temporal expression profiles under time-delay and noise
Computational Biology and Chemistry
An improved algorithm for clustering gene expression data
Bioinformatics
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Seeing the forest for the trees
Bioinformatics
Inferential Clustering Approach for Microarray Experiments with Replicated Measurements
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust Bayesian mixture modelling
Neurocomputing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.00 |
Experimental scientific data sets, especially biology data, usually contain replicated measurements. The replicated measurements for the same object are correlated, and this correlation must be carefully dealt with in scientific analysis. In this paper, we propose a robust Bayesian mixture model for clustering data sets with replicated measurements. The model aims not only to accurately cluster the data points taking the replicated measurements into consideration, but also to find the outliers (i.e., scattered objects) which are possibly required to be studied further. A tree-structured variational Bayes (VB) algorithm is developed to carry out model fitting. Experimental studies showed that our model compares favorably with the infinite Gaussian mixture model, while maintaining computational simplicity. We demonstrate the benefits of including the replicated measurements in the model, in terms of improved outlier detection rates in varying measurement uncertainty conditions. Finally, we apply the approach to clustering biological transcriptomics mRNA expression data sets with replicated measurements.