text: Similarity metrics are a core component of many information retrieval and machine learning systems. In this work we propose a method capable of learning a similarity metric from data equipped with a binary relation. By considering only the similarity constraints, and initially ignoring the features, we are able to learn target vectors for each instance using one of several appropriately designed loss functions. A regression model can then be constructed that maps novel feature vectors to the same target vector space, resulting in a feature extractor that computes vectors for which a predefined metric is a meaningful measure of similarity. We present results on both multiclass and multi-label classification datasets that demonstrate considerably faster convergence, as well as higher accuracy on the majority of the intrinsic evaluation tasks and all extrinsic evaluation tasks.
  @cite_16: A dental model trimmer having an easily replaceable abrasive surfaced member. The abrasive surfaced member is contained within a housing and is releasably coupled onto a back plate assembly which is driven by a drive motor. The housing includes a releasably coupled cover plate providing access to the abrasive surfaced member. An opening formed in the cover plate exposes a portion of the abrasive surface so that a dental model workpiece can be inserted into the opening against the abrasive surface to permit work on the dental model workpiece. A tilting work table beneath the opening supports the workpiece during the operation. A stream of water is directed through the front cover onto the abrasive surface and is redirected against this surface by means of baffles positioned inside the cover plate. The opening includes a beveled boundary and an inwardly directed lip permitting angular manipulation of the workpiece, better visibility of the workpiece and maximum safety.
summary: There is also an extension of NCA to nonlinear transformations of the input data @cite_16 . This method can be viewed as a probabilistic variant of Siamese networks. The nonlinear transformation models used in the original exposition of this method were stacked Restricted Boltzmann Machines, initialised using unsupervised pretraining and subsequently fine-tuned using the NCA loss function.