In this paper, the problem of clustering data points that lie near or on a union independent low-dimensional subspaces is addressed. To end, popular spectral clustering-based algorithms usually follow two-stage strategy initially builds an affinity matrix and then applies clustering. However, inappropriate does not sufficiently connect lying same subspace will easily lead to issue over-segmenta...

A nearest neighbor representation of a Boolean function is set positive and negative prototypes in Rn such that the has value 1 on an input iff closest prototype positive. For k-nearest majority classification k considered. The complexity minimal number needed to represent function. We give several bounds for this measure. Separations are given between cases when can be real or required Boolean...

A k nearest neighbor (kNN) classifier classifies a query instance to the most frequent class of its k nearest neighbors in the training instance space. For imbalanced class distribution, a query instance is often overwhelmed by majority class instances in its neighborhood and likely to be classified to the majority class. We propose to identify exemplar minority class training instances and gen...

Many sophisticated classification algorithms have been proposed. However, there is no clear methodology of comparing the results among different methods. According to our experiments on the popular datasets, k-NN with properly tuned parameters performs on average best. Tuning the parametres include the proper k, proper distance measure and proper weighing functions. k-NN has a zero training tim...