说明：Feature selection is one of the most important
dimension reduction techniques for its efficiency and interpretation.
Since practical data in large scale are usually collected
without labels, and labeling these data are dramatically expensive
and time <weixin_42677748> 上传 | 大小：854kb
说明：In recent years, spectral clustering has become one of the most popular modern clustering
algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software,
and very often outperforms traditional clustering algorith <weixin_42677748> 上传 | 大小：421kb
说明：Many pattern analysis and data mining problems have witnessed high-dimensional data represented by a large number of
features, which are often redundant and noisy. Feature selection is one main technique for dimensionality reduction that involves
ide <weixin_42677748> 上传 | 大小：2mb
说明：In supervised learning scenarios, feature selection has been studied
widely in the literature. Selecting features in unsupervised learning scenarios
is a much harder problem, due to the absence of class labels that
would guide the search for relevant <weixin_42677748> 上传 | 大小：186kb
说明：Sparse learning has been proven to be a powerful technique
in supervised feature selection, which allows to
embed feature selection into the classification (or regression)
problem. In recent years, increasing attention
has been on applying spare lear <weixin_42677748> 上传 | 大小：494kb
