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—In hyperspectral image analysis, the principal components analysis (PCA) and the maximum noise fraction (MNF) are most commonly used techniques for dimensionality reduction (DR), referred to as PCA-DR and MNF-DR, respectively. The criteria used by the PCA-DR and the MNF-DR are data variance and signal-to-noise ratio (SNR) which are designed to measure data(More)
Manifold learning algorithms seek to find a low-dimensional parameterization of high-dimensional data. They heavily rely on the notion of what can be considered as local, how accurately the manifold can be approximated locally, and, last but not least, how the local structures can be patched together to produce the global parameterization. In this paper, we(More)
— In this paper, we want to study how natural and engineered systems could perform complex optimizations with limited computational and communication capabilities. We adopt a continuous-time dynamical system view rooted in early work on optimization and more recently in network protocol design, and merge it with the dynamic view of distributed averaging(More)
[15] J. C. Spall, " Multivariate stochastic approximation using a simultaneous perturbation gradient approximation, " IEEE Trans. A deterministic analysis of stochastic approximation with randomized directions, " IEEE Trans. Abstract—This note presents a robust adaptive control approach for a class of time-varying uncertain nonlinear systems in the strict(More)
Cooperative energy spectrum sensing has been proved effective to detect the spectrum holes in Cognitive Radio (CR). However, its performance may suffer from the noise uncertainty, which is portrayed by the SNR wall in some literatures. In this paper we analyze the spectrum sensing performance under noise uncertainty and find an alternative approach to(More)
K-means, a simple and effective clustering algorithm, is one of the most widely used algorithms in computer vision community. Traditional k-means is an iterative algorithm— in each iteration new cluster centers are computed and each data point is reassigned to its nearest center. The cluster re-assignment step becomes prohibitively expensive when the number(More)