Metric learning learns a metric function from training data to calculate the similarity or distance between samples. From the perspective of feature learning, metric learning essentially learns a new feature space by feature transformation (e.g., Mahalanobis distance metric). However, traditional metric learning algorithms are shallow, which just learn one metric space (feature transformation). Can we further learn a better metric space from the learnt metric space? In other words, can we learn metric progressively and nonlinearly like deep learning by just using the existing metric learning algorithms? To this end, we present a hierarchical metric learning scheme and implement an online deep metric learning framework, namely ODML. Specifically, we take one online metric learning algorithm as a metric layer, followed by a nonlinear layer (i.e., ReLU), and then stack these layers modelled after the deep learning. The proposed ODML enjoys some nice properties, indeed can learn metric progressively and performs superiorly on some datasets. Various experiments with different settings have been conducted to verify these properties of the proposed ODML.
翻译：计量学习从培训数据中学习一个衡量功能,以计算样本之间的相似性或距离。从特征学习的角度来看,衡量学习基本上是通过特征转换学习一个新的特征空间(例如Mahalanobis距离度)。然而,传统的计量学习算法是浅的,只是学习一个计量空间(地质变换)。我们能否从所学的计量空间中进一步学习一个更好的计量空间?换句话说,我们能否通过仅仅利用现有的计量学习算法来逐步和非线性地学习像深层次的学习一样?为此目的,我们提出了一个等级衡量学习计划,并实施了在线深度的计量学习框架,即ODML。具体地说,我们将一个在线计量学习算法作为衡量尺度层,然后是非线性层(即ReLU),然后是深层学习后模型堆叠这些层。拟议的多指标学习法具有一些不错的特性,确实可以逐步学习计量并在某些数据集上进行更高级的处理。对不同的环境进行了各种实验,以核实拟议的多指标的特性。
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