Consistency Analysis of Nearest Subspace Classifier
This work addresses the theoretical and practical performance of NSS for classification tasks, but it is incremental as it builds on existing linear model-based classifiers.
The paper tackled the generalization ability of the Nearest Subspace Classifier (NSS) by proving its strong consistency under certain assumptions, and experimental results on simulated and real datasets showed it achieves effective classification with high efficiency, particularly for large-scale data.
The Nearest subspace classifier (NSS) finds an estimation of the underlying subspace within each class and assigns data points to the class that corresponds to its nearest subspace. This paper mainly studies how well NSS can be generalized to new samples. It is proved that NSS is strongly consistent under certain assumptions. For completeness, NSS is evaluated through experiments on various simulated and real data sets, in comparison with some other linear model based classifiers. It is also shown that NSS can obtain effective classification results and is very efficient, especially for large scale data sets.