Dimensionality Reduction of Clustered Data Sets

G. Sanguinetti

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)535-540
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume30
Issue number3
DOIs
Publication statusPublished - Mar 2008

Fingerprint

Dive into the research topics of 'Dimensionality Reduction of Clustered Data Sets'. Together they form a unique fingerprint.

Cite this