… and clustering algorithms according to the … unsupervised domain adaptation and no label
information required. And the fundamentals, advantages and disadvantages, and performance …

… Feature selection and extraction methods are employed to … assessing the performance of
the developed algorithms. Cross… The goal is to select the feature and threshold that maximize …

… Unsupervised feature selection via joint local learning and group sparse regression[J]. Frontiers
… By selecting meaningful feature subsets, the performance of learning algorithms can be …

… data, the unsupervised feature selection idea is proposed, … adopted to test the performance
of the selected feature subsets in … for unbalanced gene datasets by maximize the area under …

… we used all product features in the regression, the ranking performance was not good. Hence…
, the maximum number of iteration is 20. The iteration algorithm is as Algorithm 2 shows. In …

… genetic algorithm to optimize parameters automatically. In the first stage, the feature selection
is carried out according to the maximum … space and improve the classification performance. …

… FCBF feature selection algorithm based on maximum information coefficient[J]. Journal of
Beijing University of Postsand Telecommunications, 2018,41(4):86-90. [12] 孙曙光,纪学玲,杜…

… can reduce the dimension of data and improve the learning performance of algorithms. It is
… objective function, an unsupervised feature selection algorithm based on maximum entropy …

… relationships of features and classes in feature selection. … performance than ReliefF, FAST,
Lasso and RFS. Key words … 选择 方法(Minimum redundancy maximum relevance, mRMR) 和…

… performance without domain knowledge of the problems. This is one of the reasons why we
select SVM as … Unsupervised retraining of a maximum likelihood classifier for the analysis of …