@conference {48194,
	title = {Learning a metric for class-conditional KNN.},
	booktitle = {International Joint Conference on Neural Networks, IJCNN 2016},
	year = {2016},
	month = {2016 Oct 31},
	address = {Vancouver, British Columbia, Canada},
	abstract = {<p>Na{\"\i}ve Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitive results on several computer vision benchmarks. Its central tenet is that during NN search, a query is not compared to every example in a database, ignoring class information. Instead, NN searches are performed within each class, generating a score per class. A key problem with NN techniques, including NBNN, is that they fail when the data representation does not capture perceptual (e.g. class-based) similarity. NBNN circumvents this by using independent engineered descriptors (e.g. SIFT). To extend its applicability outside of image-based domains, we propose to learn a metric which captures perceptual similarity. Similar to how Neighbourhood Components Analysis optimizes a differentiable form of KNN classification, we propose {\textquoteright}Class Conditional{\textquoteright} metric learning (CCML), which optimizes a soft form of the NBNN selection rule. Typical metric learning algorithms learn either a global or local metric. However, our proposed method can be adjusted to a particular level of locality by tuning a single parameter. An empirical evaluation on classification and retrieval tasks demonstrates that our proposed method clearly outperforms existing learned distance metrics across a variety of image and non-image datasets.</p>
},
	doi = {10.1109/IJCNN.2016.7727436},
	author = {Im, Daniel Jiwoong and Taylor, Graham W}
}
