Feature Selection Based on Confidence Machine
This addresses the challenge of feature selection in unsupervised learning for machine learning practitioners, but it appears incremental as it builds on existing filter methods with a new metric.
The paper tackles unsupervised feature selection by proposing a filter method based on Confidence Machine to estimate feature reliability, maximizing relevance and minimizing redundancy. It shows efficiency and effectiveness compared to classic methods like Laplacian Score, Pearson Correlation, and PCA on benchmark datasets, though no concrete numbers are provided.
In machine learning and pattern recognition, feature selection has been a hot topic in the literature. Unsupervised feature selection is challenging due to the loss of labels which would supply the related information.How to define an appropriate metric is the key for feature selection. We propose a filter method for unsupervised feature selection which is based on the Confidence Machine. Confidence Machine offers an estimation of confidence on a feature'reliability. In this paper, we provide the math model of Confidence Machine in the context of feature selection, which maximizes the relevance and minimizes the redundancy of the selected feature. We compare our method against classic feature selection methods Laplacian Score, Pearson Correlation and Principal Component Analysis on benchmark data sets. The experimental results demonstrate the efficiency and effectiveness of our method.