Signal Processing with the Sparseness Constraint
نویسنده
چکیده
An overview is given of the role of the sparseness constraint in signal processing problems. It is shown that this is a fundamental problem deserving of attention. This is illustrated by describing several applications where sparseness of solution is desired. Lastly, a review is given of the algorithms that are currently available for computing sparse solutions. this session. We are hopeful that collectively they will paint a more complete picture. 2. PROBLEM FORMULATION Linear Inverse problems or the problem of signal representation can be formulated as a problem of finding a solution to an underdetermined system of equations [15.7, IO],
منابع مشابه
Signal processing with the sparseness constraint
An overview is given of the role of the sparseness constraint in signal processing problems. It is shown that this is a fundamental problem deserving of attention. This is illustrated by describing several applications where sparseness of solution is desired. Lastly, a review is given of the algorithms that are currently available for computing sparse solutions.
متن کامل3D gravity data-space inversion with sparseness and bound constraints
One of the most remarkable basis of the gravity data inversion is the recognition of sharp boundaries between an ore body and its host rocks during the interpretation step. Therefore, in this work, it is attempted to develop an inversion approach to determine a 3D density distribution that produces a given gravity anomaly. The subsurface model consists of a 3D rectangular prisms of known sizes ...
متن کاملConvolutive Non-negative Matrix Factorisation with Sparseness Constraint
Discovering a parsimonious representation that reflects the structure of audio is a requirement of many machine learning and signal processing methods. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF), which is a method for finding parts-based representations of non-negative data. We present an extension to NMF that is convolutive and forces a sparseness const...
متن کاملDiscovering Convolutive Speech Phones Using Sparseness and Non-negativity
Abstract Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Non-negative Matrix Factorisation (NMF). Here, we present a convolutive NMF algorithm that includes a sparseness constraint on the activations and has multiplicative updates. In combination w...
متن کاملDiscovering Convolutive Speech Phones using Sparseness and Non-Negativity Constraints
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can be constructed by Nonnegative Matrix Factorisation (NMF), which is a method for finding parts-based representations of non-negative data. Here, we present an extension to convolutive NMF that includes a sparseness cons...
متن کامل