The Modified Forward Backward Linear Prediction, MFBLP, is powerful technique that enables an adaptive
dimensionality reduction of the data through the estimation of the frequency domain representation of the
data poles and the utilization of the ensuing transfer function for dimensionality reduction of the data. In this
work, we isolate a data-region that is expected to encompass the statistical features of a given anomalous
event relative to the statistical common data points. The isolated anomalous events are then compared with
the adaptively extracted data using the MFBLP and the comparison is utilized to isolate the anomalous
events of interest. The effects of different levels of noise are discussed in relation to dimensionality reduction
using Eigen-features alone and by using Eigen-features accompanied by MFBLP.
Vahid R. Riasati, "Utilization of the modified forward backward linear predication approach to isolate anomalous events," Proc. SPIE 10203, Pattern Recognition and Tracking XXVIII, 102030D (Presented at SPIE Defense + Security: April 12, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2264713.
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