Unsupervised Feature Selection Based on the Morisita Estimator of Intrinsic Dimension
This paper deals with a new filter algorithm for selecting the smallest subset of features carrying all the information content of a data set (i.e. for removing redundant features). It is an advanced version of the fractal dimension reduction technique, and it relies on the recently introduced Morisita estimator of Intrinsic Dimension (ID). Here, the ID is used to quantify dependencies between subsets of features, which allows the effective processing of highly non-linear data. The proposed algorithm is successfully tested on simulated and real world case studies. Different levels of sample size and noise are examined along with the variability of the results. In addition, a comprehensive procedure based on random forests shows that the data dimensionality is significantly reduced by the algorithm without loss of relevant information. And finally, comparisons with benchmark feature selection techniques demonstrate the promising performance of this new filter.
Code (0)
등록된 구현이 없습니다.
Tasks
Dimensionality Reductionfeature selectionSimilar Papers 제목 키워드 기반
Feature Selection for Regression Problems Based on the Morisita Estimator of Intrinsic Dimension
Data acquisition, storage and management have been improved, while the key factors of many phenomena are not well known. Consequently, irrelevant and redundant features artificially increase the size of datasets, which c…
feature selectionManagementregressionUnsupervised Feature Selection with Adaptive Structure Learning
The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrin…
feature selectionSecond-Order Unsupervised Feature Selection via Knowledge Contrastive Distillation
Unsupervised feature selection aims to select a subset from the original features that are most useful for the downstream tasks without external guidance information. While most unsupervised feature selection methods foc…
feature selectionAutomatically Redundant Features Removal for Unsupervised Feature Selection via Sparse Feature Graph
The redundant features existing in high dimensional datasets always affect the performance of learning and mining algorithms. How to detect and remove them is an important research topic in machine learning and data mini…
feature selectionSparse LearningOn Evaluation of Unsupervised Feature Selection for Pattern Classification
Unsupervised feature selection aims to identify a compact subset of features that captures the intrinsic structure of data without supervised label. Most existing studies evaluate the performance of methods using the sin…
Multi-Label Classification