Thesis Title: “anomaly-preserving redundancy-reduction in high-dimensional signals“.
Seminar Abstract: “Anomaly Preserving Redundancy Reduction in High-Dimensional Signals”
Seminar Presentation: “Anomaly Preserving Redundancy Reduction in High-Dimensional Signals”
Co-superviser: Dr. Meir Barzohar.
Thesis Related Publications:
- O. Kuybeda, D. Malah and M. Barzohar, “Anomaly Preserving -Optimal Dimensionality Reduction over a Grassmann Manifold“, IEEE Trans. Signal Processing, Vol 58, No. 2, Feb. 2010, pp. 544-552.
- O. Kuybeda, D. Malah and M. Barzohar, “Anomaly Preserving – Optimal Dimensionality Reduction over a Grassmann Manifold “, CC Pub. No. 748 (also published as Technion EE Pub. No. 1705), Oct. 2009, (20 pp.). (Expanded version of Journal paper).
- O. Kuybeda, D. Malah, and M. Barzohar, “Hyperspectral Channel Reduction for Local Anomaly Detection“, Proc. EUSIPCO-09, Glasgow, Scotland, August 2009. Poster
- O. Kuybeda, D. Malah, and M. Barzohar, “Global Unsupervised Anomaly Extraction and Discrimination in Hyperspectral Images via Maximum Orthogonal-Complement Analysis (MOCA)“. Eusipco-2008 Lausanne, Switzerland, August 2008. Poster
- O. Kuybeda, D. Malah and M. Barzohar, “Global Unsupervised Anomaly Extraction and Discrimination in Hypersprectral Images via Maximum Orthogonal-Complements Analysis” CCIT Report #684, February 2008, 28 pp.
- O. Kuybeda, D. Malah, and M. Barzohar, “Rank Estimation and Redundancy Reduction of High-Dimensional Noisy Signals with Preservation of Rare Vectors“, IEEE Trans. Signal Processing, vol 55, no. 12, December 2007.
- O. Kuybeda, D. Malah, and M. Barzohar, “Rank Estimation and Redundancy Reduction of High-Dimensional Noisy Signals with Preservation of Rare Vectors“, CC Pub. No. 623, May 2007 (31 pp.)