Local Graph Reconstruction for Parameter Free Unsupervised Feature Selection

Facing with the absence of supervised information to guide the search of relevant features and the grid-search of model/hyper-parameters, it is more preferred to develop parameter-free methods and avoid additional hyper-parameters tuning. In this paper, we propose a new simple and effective parameter-free unsupervised feature selection algorithm by minimizing the linear reconstruction weight between the nearest neighbor graphs constructed from all candidate features and each single feature. The obtained global optimal reconstruction weights actually select those features with highest relevance and lowest redundancy simultaneously. The experimental results on many benchmark data sets demonstrate that the proposed method outperforms many of the state-of-the-art unsupervised feature selection methods.