  Most existing metric learning methods focus on learning a similarity or
distance measure relying on similar and dissimilar relations between sample
pairs. However, pairs of samples cannot be simply identified as similar or
dissimilar in many real-world applications, e.g., multi-label learning, label
distribution learning. To this end, relation alignment metric learning (RAML)
framework is proposed to handle the metric learning problem in those scenarios.
But RAML learn a linear metric, which can't model complex datasets. Combining
with deep learning and RAML framework, we propose a hierarchical relationship
alignment metric leaning model HRAML, which uses the concept of relationship
alignment to model metric learning problems under multiple learning tasks, and
makes full use of the consistency between the sample pair relationship in the
feature space and the sample pair relationship in the label space. Further we
organize several experiment divided by learning tasks, and verified the better
performance of HRAML against many popular methods and RAML framework.
