Dimensionality reduction of clustered data sets.
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution of the model is an unsupervised generalisation of linear discriminant analysis. This provides a completely new approach to one of the most established and widely used classification algorithms. The performance of the model is then demonstrated on a number of real and artificial data sets.
Sanguinetti
Guido
G
Department of Computer Science, University of Sheffield, Sheffield, UK. guido@dcs.shef.ac.uk
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity