Hyperspectral unmixing has attracted considerable attentions in recent years and some promising algorithms have been developed. In this paper, collaborative representation-based (CRU) for hyperspectral images is proposed. Different from imposing the sparseness constraint on training samples sparse representation, representation emphasizes collaboration of samples. Furthermore, its closed form s...

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...

We propose a new algorithm called DPC+ to enforce partial path consistency (PPC) on qualitative constraint networks. PPC restricts path consistency (PC) to a triangulation of the underlying constraint graph of a network. As PPC retains the sparseness of a constraint graph, it can make reasoning tasks such as consistency checking and minimal labelling of large qualitative constraint networks muc...

We adopt a simple time-dependent Q model where the spectrum deplets by the amount e−|ω|t/Q. From this spectrum, a Futterman wavelet is estimated at each time sample by spectral factorization. We form a matrix of these wavelets to build a constant Q attenuation model. We invert these matrices to remove the effect of Q on synthetic and field data sets. To stabilize the inversion, we add a sparsen...

We present the results of an empirical study of several constraint satisfaction search algorithms and heuristics. Using a random problem generator that allows us to create instances with given characteristics, we show how the relative performance of various search methods varies with the number of variables, the tightness of the constraints, and the sparseness of the constraint graph. A version...

We present the results of an empirical study of several constraint satisfaction search algorithms and heuristics. Using a random problem generator that allows us to create instances with given characteristics, we show how the relative performance of various search methods varies with the number of variables, the tightness of the constraints, and the sparseness of the constraint graph. A version...

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...

Many classification approaches first represent a test sample using the training samples of all the classes. This collaborative representation is then used to label the test sample. It was a common belief that sparseness of the representation is the key to success for this classification scheme. However, more recently, it has been claimed that it is the collaboration and not the sparseness that ...

Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers. The presence percentages of endmembers in mixed pixels are called abundance fractions. Spectral unmixing problem refers to decomposing these pixels into a set of endmembers and abundance fractions. Due to nonnegat...