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Tuesday, November 19, 2019
09:30 AM - 10:30 AM
CNLS Conference Room (TA-3, Bldg 1690)

Seminar

Machine Learning for Anomaly Detection in Multi-Dimensional Data

Stanley Rotman
Ben-Gurion University of the Negev

Multi-dimensional signals, such as Hyperspectral or Temporal Synthetic Aperture Radar, have very complicated distributions; machine learning promises to be a reasonable approach to determining structure in the data without any prior assumptions. In this talk, we will consider Non-Negative Matrix Factorization (NNMF) as a method to both determine trends in the data and to significantly reduce the number of redundant dimensions. We will use this transformed data for advanced anomaly detection.