Method based on semi-supervised local linear embedding algorithm for text classification

In order to solve the defects of local linear embedding algorithm(LLE) could only be used in unsupervised machine learning,combined this algorithm and the thinking of semi-supervised learning together,this paper proposed a method based on semi-supervised local linear embedding algorithm for text classification.Firstly,with the manifold structure of text data and some labeled samples,this algorithm revised the distance matrix in LLE algorithm by using piecewise function.Secondly,in order to achieve the purpose of dimensionality reduction,reconstructed the samples linearly by using the adjusted matrix.Then,because of shortcomings of the Euclidean distance in semi-supervised local linear embedding algorithm,improved it by proposing kernel based semi-supervised local linear embedding algorithm,which transformed and replaced Euclidean distance by Gaussian kernel function distance.Finally,the results of simulated experiments indicate these algorithms can really promote the performance of text classification.