Unsupervised Spectral Feature Selection with Local Structure Learning

Because there are many unlabeld high-dimensional data need to be processed, traditional unsupervised feature selection becomes an important and challenging issue in machine learning domain. The graph matrix that makes the selected features rely on high levels of the learned structure is usually constructed by traditionary embedded unsupervised methods. Nevertheless, data from real life usually contain a lot of noise samples and redundant...