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- Michael W. Berry, Murray Browne, Amy Nicole Langville, Victor Paúl Pauca, Robert J. Plemmons
- Computational Statistics & Data Analysis
- 2007

In this paper we discuss the development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis. The evolution and convergence properties of hybrid methods based on both sparsity and smoothness constraints for the resulting… (More)

- Farial Shahnaz, Michael W. Berry, Victor Paúl Pauca, Robert J. Plemmons
- Inf. Process. Manage.
- 2006

Amethodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in… (More)

Data analysis is pervasive throughout business, engineering and science. Very often the data to be analyzed is nonnegative, and it is often preferable to take this constraint into account in the analysis process. Here we are concerned with the application of analyzing data obtained using astronomical spectrometers, which provide spectral data which is… (More)

- Patrizio Campisi, Karen O. Egiazarian, +28 authors Josiane Zerubia
- 2007

Blind image deconvolution: theory and applications Images are ubiquitous and indispensable in science and everyday life. Mirroring the abilities of our own human visual system, it is natural to display observations of the world in graphical form. Images are obtained in areas 1 2 Blind Image Deconvolution: problem formulation and existing approaches ranging… (More)

- Kyle A. Gallivan, Robert J. Plemmons, Ahmed H. Sameh
- SIAM Review
- 1990

Scientific and engineering research is becoming increasingly dependent upon the development and implementation of efficient parallel algorithms on modern high-performance computers. Numerical linear algebra is an indispensable tool in such research and this paper attempts to collect and describe a selection of some of its more important parallel algorithms.… (More)

A survey of the development of algorithms for enforcing nonnegativity constraints in scientific computation is given. Special emphasis is placed on such constraints in least squares computations in numerical linear algebra and in nonlinear optimization. Techniques involving nonnegative low-rank matrix and tensor factorizations are also emphasized. Details… (More)

Abstract. This paper concerns the construction of a structured low rank matrix that is nearest to a given matrix. The notion of structured low rank approximation arises in various applications, ranging from signal enhancement to protein folding to computer algebra, where the empirical data collected in a matrix do not maintain either the specified structure… (More)

Very large seal@ matrix problems currently arise in the context of accurately computing the coordinates of points on the surface of the earth. Here g.eodesists adjust the approximate values of these coordinates by computing least squares solutions to large sparse systems of equations which result from relating the coordinates to certain observations such as… (More)

The identification and classification of non-imaging space objects, and ultimately the determination of their shape, function, and status, is an important but difficult problem still to be resolved. While ground-based telescopes with adaptive optics technology have been able to produce high-resolution images for a variety of spaced-based objects, current… (More)