Recent progress on variable projection methods for structured low-rank approximation
نویسندگان
چکیده
منابع مشابه
Recent progress on variable projection methods for structured low-rank approximation
Rank deficiency of a data matrix is equivalent to the existence of an exact linear model for the data. For the purpose of linear static modeling, the matrix is unstructured and the corresponding modeling problem is an approximation of the matrix by another matrix of a lower rank. In the context of linear time-invariant dynamic models, the appropriate data matrix is Hankel and the corresponding ...
متن کاملRecent progress in structured low-rank approximation
Rank deficiency of a data matrix is equivalent to the existence of an exact linear model for the data. For the purpose of linear static modeling, the matrix is unstructured and the correspondingmodeling problem is an approximation of the matrix by another matrix of a lower rank. In the context of linear time-invariant dynamic models, the appropriate data matrix is Hankel and the corresponding m...
متن کاملRecent process on structured low-rank approximation
Rank deficiency of a data matrix is equivalent to the existence of an exact linear model for the data. For the purpose of linear static modeling, the matrix is unstructured and the corresponding modeling problem is an approximation of the matrix by another matrix of a lower rank. In the context of linear time-invariant dynamic models, the appropriate data matrix is Hankel and the corresponding ...
متن کاملAuthor ’ s response to the referees ’ reports on " Recent progress in structured low - rank approximation
A subarea of low-rank approximation that is not covered in this overview is tensors low-rank approximation [WVB10, LV00]. Tensor methods are used in higher order statistical signal processing problems, such as independent component analysis, and multidimensional signal processing, such as spatiotemporal modeling and video processing, to name a few. Other areas of research on low-rank approximat...
متن کاملStructured Low Rank Approximation
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 or the desirable rank as is expected ...
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ژورنال
عنوان ژورنال: Signal Processing
سال: 2014
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2013.09.021