Combining graph embedding and sparse regression with structure low-rank representation for semi-supervised learning

Introduction Complex adaptive systems (CAS) research area is trying to establish a comprehensive and general understanding of the complex world around us (Niazi and Hussain 2013). Complex systems typically involve the generation of high dimensional data and rely on effective analysis and management of such high-dimensional data. High dimensional data exists in a wide variety of real applications, such as text mining, image retrieval, and visual object recognition. While the high performance of computers can address some of the problems of high dimensional data, for example, the time consuming problem, however, the processing of high-dimensional data often suffers from a series of other problems, such as the curse of dimensionality and the impact of noise and redundancy. Fortunately, it has been shown that the high dimensionality of the data is usually small in the intrinsic reduced space. Abstract