We present a method for training a similarity metric from data. The method can be used for recognition or verification applications where the number of categories is very large an...
We develop a supervised dimensionality reduction method, called Lorentzian Discriminant Projection (LDP), for feature extraction and classification. Our method represents the str...
We consider the problem of learning a Riemannian metric associated with a given differentiable manifold and a set of points. Our approach to the problem involves choosing a metric...
In this paper we describe an approach that both creates crosslingual acoustic monophone model sets for speech recognition tasks and objectively predicts their performance without ...
In plenty of scenarios, data can be represented as vectors mathematically abstracted as points in a Euclidean space. Because a great number of machine learning and data mining app...