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In this paper, we focus on subspace-based learning problems, where data elements are linear subspaces instead of vectors. To handle this kind of data, Grassmann kernels were proposed to measure the space structure and used with classifiers, e.g., Support Vector Machines (SVMs). However, the existing discriminative algorithms mostly ignore the instability of subspaces, which would cause the classifiers to be misled by disturbed instances. Thus we propose considering all potential disturbances of subspaces in learning processes to obtain more robust classifiers.
In KDD’18, 2018

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Keras provides very convenient tools for fast protyping Machine Learning models, especially neural networks. You can pass metric functions when compiling a model, to evaluate the learnt models. However in the current version (after v2.0.0), Keras no longer provides widely used binary-classification metrics, e.g., recall, f1score, etc. The reason is clearly explained in keras issue #5794. In this posts, we are going to dicuss a working-around to evaluate these metrics with Keras.

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