Sanguinetti, G. (2008) Dimensionality reduction of clustered data sets. IEEE Transactions Pattern Analysis and Machine Intelligence, 30 (3). pp. 535-540. ISSN 0162-8828Full text available as:
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.
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|Institution:||The University of Sheffield|
|Academic Units:||The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)|
|Depositing User:||Sherpa Assistant|
|Date Deposited:||08 Feb 2008 16:42|
|Last Modified:||08 Feb 2013 16:55|