We consider the problem of detecting a change in an arbitrary vector process by examining the evolution of calculated data subspaces. In our developments, both the data subspaces and the change identification criterion are novel and founded in the theory of L1-norm principal-component analysis (PCA). The outcome is highly accurate, rapid detection of change in streaming data that vastly outperforms conventional eigenvector subspace methods (L2-norm PCA). In this paper, illustrations are offered in the context of artificial data and real electroencephalography (EEG) and electromyography (EMG) data sequences.

Detection of change by L1-norm principal-component analysis / Gallone, G.; Varma, K.; Pados, D. A.; Colonnese, S.. - 11395:(2020). (Intervento presentato al convegno Big Data II: Learning, Analytics, and Applications 2020 tenutosi a usa) [10.1117/12.2559976].

Detection of change by L1-norm principal-component analysis

Gallone G.;Colonnese S.
2020

Abstract

We consider the problem of detecting a change in an arbitrary vector process by examining the evolution of calculated data subspaces. In our developments, both the data subspaces and the change identification criterion are novel and founded in the theory of L1-norm principal-component analysis (PCA). The outcome is highly accurate, rapid detection of change in streaming data that vastly outperforms conventional eigenvector subspace methods (L2-norm PCA). In this paper, illustrations are offered in the context of artificial data and real electroencephalography (EEG) and electromyography (EMG) data sequences.
2020
Big Data II: Learning, Analytics, and Applications 2020
detection of change; L1-norm; principal-component analysis; streaming data; time series
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Detection of change by L1-norm principal-component analysis / Gallone, G.; Varma, K.; Pados, D. A.; Colonnese, S.. - 11395:(2020). (Intervento presentato al convegno Big Data II: Learning, Analytics, and Applications 2020 tenutosi a usa) [10.1117/12.2559976].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1443493
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