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 / IODICE D'ENZA, Alfonso; Angelos, Markos. - (2015), pp. 137-148. [10.1007/978-3-319-17377-1_15]

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.
2015
9783319173764
Incremental visualization of categorical data / IODICE D'ENZA, Alfonso; Angelos, Markos. - (2015), pp. 137-148. [10.1007/978-3-319-17377-1_15]
File in questo prodotto:
File Dimensione Formato  
Incremental visualization of categorical data_OK_commissione.pdf

solo utenti autorizzati

Tipologia: Documento in Post-print
Licenza: Accesso privato/ristretto
Dimensione 352.7 kB
Formato Adobe PDF
352.7 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/744252
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact