A DIMENSION REDUCTION ALGORITHM FOR MULTI LOCAL LINEAR PATTERN PRESERVING
In order to capture the local nonlinear structure of data more accurately,an unsupervised dimension reduction algorithm based on multiple local linear pattern preserving is proposed.The corresponding data points were reconstructed linearly in the local region,and the directional derivative was used to replace the gradient in the first-order Taylor expansion to reduce the approximation error.The multi-linear patterns were used to represent the data points,so as to describe the local nonlinear geometric features of the data more accurately.Furthermore,the embedding result was obtained by minimizing the multi local linear reconstruction error in the embedded data space.The experimental results on 4 synthetic datasets and 6 real datasets show that the proposed method can accurately capture the nonlinear structure.
Local nonlinearityUnsupervisedDimension reductionLinear reconstruction
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