OBTAINING OF SPARSE SOLUTIONS WITH D-OPTIMAL PARTITIONING OF ORIGINAL SAMPLE INTO TRAINING AND TEST PARTS AND REGULARITY CRITERION
The article considers the methods of obtaining sparse solutions based on the LS-SVM method. The training samples generated according to the D-optimal experiment design are used for obtaining a sparse solution. An algorithm for forming a test sample using the regularity criterion is also presented. A computational experiment is conducted to check the efficiency of the proposed methods of forming a training sample. The artificial data is used for this purpose. The improvement of the generalizing properties of the models obtained by LS-SVM is carried out through the selection of the Gaussian kernel function parameter while minimizing the prediction error. The kernel function parameter is chosen according to the minimum of external criteria. The criteria of cross-validation and regularity are used as external criteria for assessing the quality of models. The effectiveness of the obtained solutions is estimated by mean square error. The noise variance (noise level) is set as a percentage of the signal power during the data modeling. According to the results, conclusions are made that to receive a sparse solution it is possible to use a training sample, obtained with the use of D-optimal split. The additional optimization of the composition of points (exchange algorithm) is possible to use as well, which in some cases allows getting the best solutions.