AutoEncoder Inspired Unsupervised Feature Selection
This addresses the computational and analytical challenges of high-dimensional data in fields like computer vision and machine learning, offering a more flexible approach than traditional linear methods, though it appears incremental as it builds on existing autoencoder and group lasso techniques.
The paper tackles the problem of unsupervised feature selection for high-dimensional data by proposing AutoEncoder Feature Selector (AEFS), which combines autoencoder regression and group lasso to select important features by capturing both linear and nonlinear information, resulting in superior performance compared to state-of-the-art methods on benchmark datasets.
High-dimensional data in many areas such as computer vision and machine learning tasks brings in computational and analytical difficulty. Feature selection which selects a subset from observed features is a widely used approach for improving performance and effectiveness of machine learning models with high-dimensional data. In this paper, we propose a novel AutoEncoder Feature Selector (AEFS) for unsupervised feature selection which combines autoencoder regression and group lasso tasks. Compared to traditional feature selection methods, AEFS can select the most important features by excavating both linear and nonlinear information among features, which is more flexible than the conventional self-representation method for unsupervised feature selection with only linear assumptions. Experimental results on benchmark dataset show that the proposed method is superior to the state-of-the-art method.