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Learning robust regression model from high-dimensional corrupted data is an essential and difficult problem in many practical applications. The state-of-the-art methods have studied low-rank regression models that are robust against typical noises (like Gaussian noise and out-sample sparse noise) or outliers, such that a regression model can be learned from clean data lying on underlying subspaces...
Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional...
Stereo matching is an extensively researched topic in computer vision. Stereo matching algorithms are essential for recovering depth information of objects. Existing state-of-the-art stereo methods require very high processing times. Consequently, we cannot employ them in commercial applications though they are very accurate and robust. With a view to reduce the computation time this paper presents...
Asset prices fluctuate up and down chaotically. Traders, investors and fund managers comb the chaos for exploitable patterns with methods such as moving averages from the realm of technical analysis. In this paper we focus on linear moving averages which aim to smooth asset prices removing fluctuations. First, we will develop a method to measure the smoothness for a linear filter. We will also discuss...
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