Random projections (RPs) have shown to provide promising results in the context of high-dimensional supervised classification. In this work, we address the unsupervised classification issue by exploiting the general idea of RP ensemble. Specifically, we generate a set of low dimensional independent random projections and we perform a model-based clustering on each of them. The top B* projections, i.e. the projections which show the best grouping structure, are then retained. The final partition is obtained by aggregating the chosen classifiers via consensus. The performances of the method are assessed on a set of both real and simulated data.

laura anderlucci, francesca fortunato, angela montanari (2019). High-dimensional model-based clustering via random projections. Centro Editoriale di Ateneo Università di Cassino e del Lazio Meridionale.

High-dimensional model-based clustering via random projections

laura anderlucci;francesca fortunato
;
angela montanari
2019

Abstract

Random projections (RPs) have shown to provide promising results in the context of high-dimensional supervised classification. In this work, we address the unsupervised classification issue by exploiting the general idea of RP ensemble. Specifically, we generate a set of low dimensional independent random projections and we perform a model-based clustering on each of them. The top B* projections, i.e. the projections which show the best grouping structure, are then retained. The final partition is obtained by aggregating the chosen classifiers via consensus. The performances of the method are assessed on a set of both real and simulated data.
2019
CLADAG 2019 - Book of short papers
38
41
laura anderlucci, francesca fortunato, angela montanari (2019). High-dimensional model-based clustering via random projections. Centro Editoriale di Ateneo Università di Cassino e del Lazio Meridionale.
laura anderlucci; francesca fortunato; angela montanari
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/712136
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