Non-parametric classifier-independent feature selection
Pattern Recognition
Weighted locally linear embedding for dimension reduction
Pattern Recognition
A class discriminality measure based on feature space partitioning
Pattern Recognition
Kernel-based metric learning for semi-supervised clustering
Neurocomputing
Expert Systems with Applications: An International Journal
A training sample sequence planning method for pattern recognition problems
Automatica (Journal of IFAC)
Hi-index | 754.84 |
Nonparametric density estimation using thek-nearest-neighbor approach is discussed. By developing a relation between the volume and the coverage of a region, a functional form for the optimumkin terms of the sample size, the dimensionality of the observation space, and the underlying probability distribution is obtained. Within the class of density functions that can be made circularly symmetric by a linear transformation, the optimum matrix for use in a quadratic form metric is obtained. For Gaussian densities this becomes the inverse covariance matrix that is often used without proof of optimality. The close relationship of this approach to that of Parzen estimators is then investigated.