Distance-learning with Seneca

Distance Metric Learning with Kernels

In this paper, we propose a feature weighting method that works in both the input space and the kernel-induced feature space. It assumes only the availability of similarity (dissimilarity) information, and the number of parameters in the transformation does not depend on the number of features. Besides feature weighting, it can also be regarded as performing nonparametric kernel adaptation. Exp...

Learning with Distance Substitution Kernels

During recent years much effort has been spent in incorporating problem specific a-priori knowledge into kernel methods for machine learning. A common example is a-priori knowledge given by a distance measure between objects. A simple but effective approach for kernel construction consists of substituting the Euclidean distance in ordinary kernel functions by the problem specific distance measu...

Seneca Valley Virus

Cleaning and Disinfecting  The efficacy of most disinfectants against SVV is not clearly known.  Because vesicular diseases are clinically indistinguishable, disinfection protocols for FMDV should be followed even if SVV is suspected. This includes use of: sodium hydroxide, sodium carbonate, 0.2% citric acid, aldehydes, and oxidizing disinfectants including sodium hypochlorite.  Below are EP...

The Seneca Amplification Construction

Deep Distance Metric Learning with Data Summarization

We present Deep Stochastic Neighbor Compression (DSNC), a framework to compress training data for instance-based methods (such as k-nearest neighbors). We accomplish this by inferring a smaller set of pseudo-inputs in a new feature space learned by a deep neural network. Our framework can equivalently be seen as jointly learning a nonlinear distance metric (induced by the deep feature space) an...