Small world neighborhood optimized local linear embedding algorithm

By analyzing the invalidity reason of the local linear embedding (LLE) algorithm in case of the sparse data or the high noise data, small world neighborhood optimization LLE algorithm (SLLE) is proposed based on the complex networks theory. The data in LLE are optimized using the small world algorithm, and the shortest path and the local neighbor set clustering coefficients are used as the local parameters. As a result, the problem of the embedding distortion using only local linear patch of the manifold to define neighborhood in Euclidean space is effectively solved. Three groups of standard data sets are selected to test and to compare the efficiency and robustness of SLLE and LLE. The experimental results show that the calculation results, robustness and dimension reduction of SLLE are all better than those of LLE, and accuracy rate of SLLE is at least 10 percent higher than that of LLE.