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.
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 (Presented at SPIE Defense + Security: April 19, 2018; Published: 30 April 2018); https://doi.org/10.1117/12.2305101.
Conference Presentations are recordings of oral presentations given at SPIE conferences and published as part of the conference proceedings. They include the speaker's narration along with a video recording of the presentation slides and animations. Many conference presentations also include full-text papers. Search and browse our growing collection of more than 12,000 conference presentations, including many plenary and keynote presentations.