Quasi Cosine Similarity Metric Learning

It is vital to select an appropriate distance metric for many learning algorithm. Cosine distance is an efficient metric for measuring the similarity of descriptors in classification task. However, the cosine similarity metric learning (CSML)[1] is not widely used due to the complexity of its formulation and time consuming. In this paper, a Quasi Cosine Similarity Metric Learning (QCSML) is proposed to make it easy. The normalization and Lagrange multipliers are employed to convert cosine distance into simple formulation, which is convex and its derivation is easy to calculate. The complexity of the QCSML algorithm is O(t×p×d), while the complexity of CSML is O(r× b×g×s×d×m). The experimental results of our method on UCI datasets for classification task and LFW dataset for face verification problem are better than the state-of-the-art methods. For classification task, the proposed approach is employed on Iris, Ionosphere and Wine dataset and the classification accuracy and the time consuming are much better than the compared methods. Moreover, our approach obtains 92.33% accuracy for face verification on unrestricted setting of LFW dataset, which outperforms the state-of-the-art algorithms.