The nearest neighbor (NN) classifier is well suited for generic object recognition. However, it requires storing the complete training data, and classification time is linear in ...
Ferid Bajramovic, Frank Mattern, Nicholas Butko, J...
We present a new approach, called local discriminant embedding (LDE), to manifold learning and pattern classification. In our framework, the neighbor and class relations of data a...
In this paper, we propose a novel method for fast nearest neighbors retrieval in non-Euclidean and non-metric spaces. We organize the data into a hierarchical fashion that preserv...
This paper presents an algorithm, called the winnerupdate algorithm, for accelerating the nearest neighbor search. By constructing a hierarchical structure ,for each feature point...
Hausdorff metrics are used in geometric settings for measuring the distance between sets of points. They have been used extensively in areas such as computer vision, pattern recog...