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Support Vector Data Description (SVDD) methods have been successfully applied to hyperspectral anomaly detection. Unfortunately, the performance of SVDD methods suffers when noisy or non-informative bands are present in the data. If a set of sparse features could be identified for these techniques, the resulting data may improve SVDD performance while enjoying the benefits of decreased processing...
The problem of overlapping clustering, where a point is allowed to belong to multiple clusters, is becoming increasingly important in a variety of applications. In this paper, we present an overlapping clustering algorithm based on multiplicative mixture models. We analyze a general setting where each component of the multiplicative mixture is from an exponential family, and present an efficient alternating...
The quantitative characterization of the cornea's ultrastructure is an important component in clinical studies and ophthalmologic research for investigating various corneal pathologies such as Fuch's dystrophy, bullous keratopathy, and macular corneal dystrophy. The spatial ordering of the stromal fibrils is a good predictor of corneal transparency and light scattering. In this paper this structure...
A support vector algorithm for detecting endmembers in a hyperspectral image is introduced. It is a novel method for finding the spectral convexities in a high-dimensional space which addresses several limitations of previous endmember methods. A new approach for estimating the number of endmembers using rate-distortion theory is also presented. It is based upon the observation that the endmembers...
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