Title :
Dimensionality Reduction of Clustered Data Sets
Author :
Sanguinetti, Guido
Author_Institution :
Univ. of Sheffield, Sheffield
fDate :
3/1/2008 12:00:00 AM
Abstract :
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 generalization 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.
Keywords :
maximum likelihood estimation; pattern classification; pattern clustering; classification algorithms; clustered data sets; linear dimensionality reduction; linear discriminant analysis; maximum likelihood solution; probabilistic latent variable model; Algorithm design and analysis; Bioinformatics; Classification algorithms; Clustering algorithms; Computer vision; Feature extraction; Independent component analysis; Linear discriminant analysis; Machine learning algorithms; Principal component analysis; clustering; dimensionality reduction; discriminant analysis; probabilistic algorithms; Algorithms; Artificial Intelligence; Cluster Analysis; Databases, Factual; Information Storage and Retrieval; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2007.70819
