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This paper proposed a novel feature selection method that includes a self-representation loss function, a graph regularization term and an $${l_{2,1}}$$ l 2 , 1 -norm regularization term. Different from traditional least square loss function which focuses on achieving the minimal regression error between the class labels and their corresponding predictions, the proposed self-representation...
A novel feature selection algorithm is designed for high-dimensional data classification. The relevant features are selected with the least square loss function and $${\ell _{2,1}}$$ ℓ 2 , 1 -norm regularization term if the minimum representation error rate between the features and labels is approached with respect to only these features. Taking into account both the local and global structures...
The Random Forests algorithm belongs to the class of ensemble learning methods, which are common used in classification problem. In this paper, we studied the problem of adopting the Random Forests algorithm to learn raw data from real usage scenario. An improvement, which is stable, strict, high efficient, data-driven, problem independent and has no impact on algorithm performance, is proposed to...
In this paper, we perform an exploring on music characteristics of Chinese folk music, in order to do targeted music restoration and perform digital reproduction on music segments. Starting from the point of music classification and music feature selection, we firstly choose SVM as the classifier according to experiment results of different classifiers. Then we introduce two common filter-filter methods:...
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