Optimal subspace classification method for complex data

KNNModel algorithm is an improved version for k-nearest neighbor method. However, it has the problem of high time complexity and lower performance when dealing with complex data. An optimal subspace classification method called IKNNModel is proposed in this paper by projecting different training samples onto their own optimal subspace and constructing the corresponding class cluster and pure cluster as the basis of classification. For datasets with complex structure, that is, the training samples from different categories are overlapped with one another on the original space or have a high dimensionality, the proposed method can construct the corresponding clusters for the overlapped samples on their own subspaces easily. Experimental results show that compared with KNNModel, the proposed method not only significantly improves the classification performance on datasets with complex structure, but also improves the efficiency of the classification.