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Resent years, sparse representation theory has been widely used in signal processing field. Researchers introduce this theory into the application of pattern recognition and classification and get the sparse representation classifier (SRC). In this paper, we use the SRC to achieve the classification of LiDAR (Light Detection and Ranging) points. To get a better performance, we introduce the kernel...
In many practical machine learning systems, the prediction/classification tasks involve the usage of heterogeneous data in semi-supervised settings, where the objective is to maximize the utility of multiple views (usually dual views) information from the data. In this work, we propose a general framework, Dual Uncertainty Minimization Regularization (DUMR), that maximizes the usage of heterogeneous...
In the literature of feature selection, different criteria have been proposed to evaluate the goodness of features. In our investigation, we notice that a number of existing selection criteria implicitly select features that preserve sample similarity, and can be unified under a common framework. We further point out that any feature selection criteria covered by this framework cannot handle redundant...
With the increasing use of digital monitor technique, requirements for image interpolation have become more critical. Especially, the development of LCD screen sizes has grown up. Conventional interpolation methods have several drawbacks, such as blurring or blocky effects. Many edge detect methods have been widely used to avoid these problems but are notorious for high complexity and cost. Therefore,...
There are many applications where multiple data sources, each with its own features, are integrated in order to perform an inference task in an optimal way. Researchers have shown that for many tasks like webpage classification, image classification, and pattern recognition, combining data from multiple information sources yields significantly better results than using a single source. In these tasks...
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