Threshold Auto-Tuning Metric Learning

7 Jan 2018 Yuya Onuma Rachelle Rivero Tsuyoshi Kato

It has been reported repeatedly that discriminative learning of distance metric boosts the pattern recognition performance. A weak point of ITML-based methods is that the distance threshold for similarity/dissimilarity constraints must be determined manually and it is sensitive to generalization performance, although the ITML-based methods enjoy an advantage that the Bregman projection framework can be applied for optimization of distance metric... (read more)

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