Generalized Regression Neural Network Optimized by Genetic Algorithm for Solving Out-of-Sample Extension Problem in Supervised Manifold Learning
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With the advent of big data, massive amounts of high-dimensional data have been accumulated in many fields. The assimilation and processing of such high-dimensional data can be particularly challenging. Manifold learning offers a means for effectively dealing with this challenge. However, the results of applying manifold learning to supervised classification have remained unsatisfactory. The out-of-sample extension problem is a critical issue that must be properly solved in this regard. Genetic algorithms (GAs) have excellent global search capabilities. This paper proposes a generalized regression neural network (GRNN) optimized by a GA for the solution of the out-of-sample extension problem. The prediction performance of a GRNN mainly depends on the appropriateness of the chosen smoothing factor. The essence of the GA optimization is the determination of the optimal smoothing factor of the GRNN, the optimized form of which is subsequently used to forecast the low-dimensional embeddings of the test samples. A GA can be used to obtain a better smoothing factor in a larger search space, resulting in enhanced prediction performance. Experiments were performed to enable a detailed analysis of the important parameters that affect the performance of the proposed algorithm. The results confirmed the effectiveness of the algorithm.
KeywordsManifold learning Dimensionality reduction Out-of-sample extension Genetic algorithm Generalized regression neural network Optimization
The authors would like to thank anonymous referees as well as the Associate Editor for their constructive comments and suggestions. They would also like to thank Editage (www.editage.com) for English language editing. This work was partially supported by the Natural Science Foundation of Fujian Province, China, under Grant 2016J01279, and the Natural Science Foundation of Education Department of Fujian Province, China, under Grant JB14003.
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