Unsupervised Feature Selection Based on Space Filling Concept
The paper deals with the adaptation of a new measure for the unsupervised feature selection problems. The proposed measure is based on space filling concept and is called the coverage measure. This measure was used for judging the quality of an experimental space filling design. In the present work, the coverage measure is adapted for selecting the smallest informative subset of variables by reducing redundancy in data. This paper proposes a simple analogy to apply this measure. It is implemented in a filter algorithm for unsupervised feature selection problems. The proposed filter algorithm is robust with high dimensional data and can be implemented without extra parameters. Further, it is tested with simulated data and real world case studies including environmental data and hyperspectral image. Finally, the results are evaluated by using random forest algorithm.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionSimilar Papers 제목 키워드 기반
Online Unsupervised Multi-view Feature Selection
In the era of big data, it is becoming common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view data are usually unlabeled and come from high-dimensional spaces…
Clusteringfeature selectionSparse LearningBinary Space Partitioning as Intrinsic Reward
An autonomous agent embodied in a humanoid robot, in order to learn from the overwhelming flow of raw and noisy sensory, has to effectively reduce the high spatial-temporal data dimensionality. In this paper we propose a…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Subspace Learning for Feature Selection via Rank Revealing QR Factorization: Unsupervised and Hybrid Approaches with Non-negative Matrix Factorization and Evolutionary Algorithm
The selection of most informative and discriminative features from high-dimensional data has been noticed as an important topic in machine learning and data engineering. Using matrix factorization-based techniques such a…
feature selectionUnsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering
Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms. In recent years, a variety of efforts are investigated on feature selection problems bas…
Clusteringfeature selectionRepresentation LearningGlobal and Local Structure Preserving Sparse Subspace Learning: An Iterative Approach to Unsupervised Feature Selection
As we aim at alleviating the curse of high-dimensionality, subspace learning is becoming more popular. Existing approaches use either information about global or local structure of the data, and few studies simultaneousl…
feature selection