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].
Titolo: | Detection of change by L1-norm principal-component analysis | |
Autori: | ||
Data di pubblicazione: | 2020 | |
Serie: | ||
Citazione: | 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]. | |
Handle: | http://hdl.handle.net/11573/1443493 | |
Appartiene alla tipologia: | 04b Atto di convegno in volume |
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