Multiple correspondence analysis (MCA) is a well-established dimension reduction method to explore the associations within a set of categorical variables and it consists of a singular value decomposition (SVD) of a suitably transformed matrix. The high computational and memory requirements of ordinary SVD make its application impractical on massive or sequential data sets that characterize several modern applications. The aim of the present contribution is to allow for incremental updates of existing MCA solutions, which lead to an approximate yet highly accurate solution; this makes it possible to track, via MCA, the association structures in data flows. To this end, an incremental SVD approach with desirable properties is embedded in the context of MCA.

Incremental visualization of categorical data

Alfonso Iodice D’Enza
;
2015

Abstract

Multiple correspondence analysis (MCA) is a well-established dimension reduction method to explore the associations within a set of categorical variables and it consists of a singular value decomposition (SVD) of a suitably transformed matrix. The high computational and memory requirements of ordinary SVD make its application impractical on massive or sequential data sets that characterize several modern applications. The aim of the present contribution is to allow for incremental updates of existing MCA solutions, which lead to an approximate yet highly accurate solution; this makes it possible to track, via MCA, the association structures in data flows. To this end, an incremental SVD approach with desirable properties is embedded in the context of MCA.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/744252
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