Stochastic gradient descent implementation of the modified forward-backward linear prediction

Vahid R. Riasati; Patrick G. Schuetterle; Christopher O'hara

doi:10.1117/12.2305101

30 April 2018 Stochastic gradient descent implementation of the modified forward-backward linear prediction

Vahid R. Riasati, Patrick G. Schuetterle, Christopher O'hara

Proceedings Volume 10649, Pattern Recognition and Tracking XXIX; 106490R (2018) https://doi.org/10.1117/12.2305101
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

The Modified Forward Backward Linear Prediction, MFBLP, is an effective method for data dimensionality reduction and combined with eigen-vector and eigen-value techniques significant improvements in signal isolation have been shown and discussed in previous notes of this technique. In the present work, a Stochastic Gradient Descent technique is utilized to limit the dimensionality reduction of the MFBLP and the results of this technique is compared in relation to an application of the eigen-vector eigen-value technique to limit the dimensionality reduction of the MFBLP. By using a correlation metric we are able to discuss the measure of goodness of the new implementation of the MFBLP, discuss its potential, and some of its applications in this analysis. The processing approach is for active sensor systems and discussed for comparison.

Conference Presentation

Citation Download Citation

Vahid R. Riasati, Patrick G. Schuetterle, and Christopher O'hara "Stochastic gradient descent implementation of the modified forward-backward linear prediction", Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 106490R (30 April 2018); https://doi.org/10.1117/12.2305101

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