Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
On nonmetric similarity search problems in complex domains
ACM Computing Surveys (CSUR)
A novel prototype generation technique for handwriting digit recognition
Pattern Recognition
| Hi-index | 0.00 |
Fast nearest neighbor (NN) finding has been extensively studied. While some fast NN algorithms using metrics rely on the essential properties of metric spaces, the others usingnon-metric measures fail for large-size templates. However, in some applications with very large size templates, the best performance is achieved by NN methods based on the dissimilaritymeasures resulting in a special space where computations cannot be pruned by the algorithms based-on the triangular inequality. For such NN methods, the existing fast algorithms except condensing algorithms are not applicable. In this paper, a fast hierarchical search algorithm is proposed to find k-NNs using a non-metric measure in a binary feature space. Experiments with handwritten digit recognition show that the new algorithm reduces on average dissimilarity computations by more than 90% while losing the accuracy by less than 0:1%, with a 10% increase in memory.