Unsupervised feature selection algorithm framework based on neighborhood interval disturbance fusion
This work addresses the stability issue in unsupervised feature selection for data dimensionality reduction, but it appears incremental as it builds on existing frameworks without introducing a paradigm shift.
The paper tackled the problem of low universality and stability in unsupervised feature selection by proposing a new algorithm based on neighborhood interval disturbance fusion (NIDF), which preprocesses datasets with an interval method and achieves joint learning of feature scores and approximate data intervals, though no concrete performance numbers are provided.
Feature selection technology is a key technology of data dimensionality reduction. Becauseof the lack of label information of collected data samples, unsupervised feature selection has attracted more attention. The universality and stability of many unsupervised feature selection algorithms are very low and greatly affected by the dataset structure. For this reason, many researchers have been keen to improve the stability of the algorithm. This paper attempts to preprocess the data set and use an interval method to approximate the data set, experimentally verifying the advantages and disadvantages of the new interval data set. This paper deals with these data sets from the global perspective and proposes a new algorithm-unsupervised feature selection algorithm based on neighborhood interval disturbance fusion(NIDF). This method can realize the joint learning of the final score of the feature and the approximate data interval. By comparing with the original unsupervised feature selection methods and several existing feature selection frameworks, the superiority of the proposed model is verified.