2026-09-19T17:57:20Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/284572022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David H. Staelin.
Mueller, Amy, 1980-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-26T20:34:16Z
2005-09-26T20:34:16Z
2003
2003
http://hdl.handle.net/1721.1/28457
57031869
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 125-126).
A method for blind separation of noisy jointly Gaussian multivariate signals X is presented, where X = AP + N, X is an observed set of vectors, A is the mixing matrix, P is the unknown signal matrix, and N is white noise. The objective is to estimate all matrices on the right-hand side when even their dimensions (the system order) are unknown. The algorithms developed are extensions of the Iterative Order and Noise (ION) estimation algorithm [10]. Improvements made within the iterative structure of ION to better estimate the order and noise yield ION'. The addition of a second-order blind identification algorithm (SOBI, [4]) subsequently yields ONA, which fully characterizes a data set by estimating the (O)rder, (N)oise, and mixing matrix (A). Metrics are developed to evaluate the performance of these algorithms, and their applicability is discussed. Optimum algorithm constants for ION' and ONA are derived, and their range of applicability is outlined. The algorithms are evaluated through application to three types of data: (1) simulated Gaussian data which spans the problem space, (2) a set of non-Gaussian factory data with 577 variables, and (3) a hyperspectral image with 224 channels. The ONA algorithm is extended to 2D (spatial) hyperspectral problems by exploiting spatial rather than time correlation. ONA produces a full characterization of the data with high signal-to-noise ratios for most unknown parameters in the Gaussian case, though the jointly Gaussian P is shown to be most difficult to retrieve. In all three cases, ONA reduces the noise in the data, identifies small sets of highly correlated variables, and unmixes latent signals. The spatial ONA identifies surface features in the hyperspectral image and retrieves sources
(cont.) significantly more independent than those retrieved by PCA. Further exploration of the applicability of these algorithms to other types of data and further algorithmic improvement is recommended.
by Amy Mueller.
M.Eng.
126 p.
5589269 bytes
5604784 bytes
application/pdf
application/pdf
en_US
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Iterative blind separation of Gaussian data of unknown order
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<Abstract>A method for blind separation of noisy jointly Gaussian multivariate signals X is presented, where X = AP + N, X is an observed set of vectors, A is the mixing matrix, P is the unknown signal matrix, and N is white noise. The objective is to estimate all matrices on the right-hand side when even their dimensions (the system order) are unknown. The algorithms developed are extensions of the Iterative Order and Noise (ION) estimation algorithm [10]. Improvements made within the iterative structure of ION to better estimate the order and noise yield ION'. The addition of a second-order blind identification algorithm (SOBI, [4]) subsequently yields ONA, which fully characterizes a data set by estimating the (O)rder, (N)oise, and mixing matrix (A). Metrics are developed to evaluate the performance of these algorithms, and their applicability is discussed. Optimum algorithm constants for ION' and ONA are derived, and their range of applicability is outlined. The algorithms are evaluated through application to three types of data: (1) simulated Gaussian data which spans the problem space, (2) a set of non-Gaussian factory data with 577 variables, and (3) a hyperspectral image with 224 channels. The ONA algorithm is extended to 2D (spatial) hyperspectral problems by exploiting spatial rather than time correlation. ONA produces a full characterization of the data with high signal-to-noise ratios for most unknown parameters in the Gaussian case, though the jointly Gaussian P is shown to be most difficult to retrieve. In all three cases, ONA reduces the noise in the data, identifies small sets of highly correlated variables, and unmixes latent signals. The spatial ONA identifies surface features in the hyperspectral image and retrieves sources</Abstract>
<Abstract>(cont.) significantly more independent than those retrieved by PCA. Further exploration of the applicability of these algorithms to other types of data and further algorithmic improvement is recommended.</Abstract>
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