Reviews: Bilevel Distance Metric Learning for Robust Image Recognition

Summary: The authors propose a bilevel method for metric learning, where the lower level is responsible for the extraction of discriminative features from the data based on a sparse coding scheme with graph regularization. This effectively detects their underlying geometric structure, and the upper level is a classic metric learning approach that utilizes the learned sparse coefficients. These two components are integrated into a joint optimization problem and an efficient optimization algorithm is developed accordingly. Hence, new data can be classified based on the learned dictionary and the corresponding metric. In the experiments the authors demonstrate the capabilities of the model to provide more discriminative features from high dimensional data, while being more robust to noise.