Two Projection Pursuit Algorithms for Machine Learning under Non-Stationarity

Two Projection Pursuit Algorithms for Machine Learning under Non-Stationarity

This thesis derives, tests and applies two linear projection algorithms for machine learning under non-stationarity. The first finds a direction in a linear space upon which a data set is maximally non-stationary. The second aims to robustify two-way classification against non-stationarity. The algo...

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Bibliographische Detailangaben
1. Verfasser: Blythe, Duncan A. J
Format: Artikel
Sprache:eng
Schlagworte:
Computer Science - Artificial Intelligence
Computer Science - Learning
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Zusammenfassung:This thesis derives, tests and applies two linear projection algorithms for machine learning under non-stationarity. The first finds a direction in a linear space upon which a data set is maximally non-stationary. The second aims to robustify two-way classification against non-stationarity. The algorithm is tested on a key application scenario, namely Brain Computer Interfacing.
DOI:10.48550/arxiv.1110.0593