Neighborhood MinMax Projections

View Researcher II's Other Codes

Disclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”

Please contact us in case of a broken link from here

Authors Feiping Nie, Shiming Xiang, Changshui Zhang
Journal/Conference Name The Twentieth International Joint Conference on Artificial Intelligence (IJCAI)
Paper Category
Paper Abstract A new algorithm, Neighborhood MinMax Projections (NMMP), is proposed for supervised dimensionality reduction in this paper. The algorithm aims at learning a linear transformation, and focuses only on the pairwise points where the two points are neighbors of each other. After the transformation, the considered pairwise points within the same class are as close as possible, while those between different classes are as far as possible. We formulate this problem as a constrained optimization problem, in which the global optimum can be effectively and efficiently obtained. Compared with the popular supervised method, Linear Discriminant Analysis (LDA), our method has three significant advantages. First, it is able to extract more discriminative features. Second, it can deal with the case where the class distributions are more complex than Gaussian. Third, the singularity problem existing in LDA does not occur naturally. The performance on several data sets demonstrates the effectiveness of the proposed method.
Date of publication 2007
Code Programming Language MATLAB
Comment

Copyright Researcher II 2021