Fractal Autoencoders for Feature Selection
This addresses the problem of high-dimensional data analysis for researchers and practitioners, offering an incremental improvement in feature selection methods.
The paper tackles unsupervised feature selection by proposing fractal autoencoders (FAE), which achieve state-of-the-art performance on fourteen datasets, including reducing measurement cost by about 15% on gene expression data compared to L1000 landmark genes.
Feature selection reduces the dimensionality of data by identifying a subset of the most informative features. In this paper, we propose an innovative framework for unsupervised feature selection, called fractal autoencoders (FAE). It trains a neural network to pinpoint informative features for global exploring of representability and for local excavating of diversity. Architecturally, FAE extends autoencoders by adding a one-to-one scoring layer and a small sub-neural network for feature selection in an unsupervised fashion. With such a concise architecture, FAE achieves state-of-the-art performances; extensive experimental results on fourteen datasets, including very high-dimensional data, have demonstrated the superiority of FAE over existing contemporary methods for unsupervised feature selection. In particular, FAE exhibits substantial advantages on gene expression data exploration, reducing measurement cost by about $15$\% over the widely used L1000 landmark genes. Further, we show that the FAE framework is easily extensible with an application.