| |
|
|
Tuesday, November 19, 201909:30 AM - 10:30 AMCNLS Conference Room (TA-3, Bldg 1690) Seminar Machine Learning for Anomaly Detection in Multi-Dimensional Data Stanley RotmanBen-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.
|