摘要
近邻与稀疏保持投影已被广泛应用于降维方法,通过优化得到满足近邻结构或稀疏结构的降维投影矩阵,然而这类方法多数只考虑单一结构特征.此外,多数非线性降维方法无法求出显式的映射函数,极大地限制了降维方法的应用.为克服这些问题,本文借鉴极限学习机的思想,提出面向聚类的基于稀疏和近邻保持的极限学习机降维算法(SNP-ELM). SNP-ELM算法是一种非线性无监督降维方法,在降维过程中同时考虑数据的稀疏结构与近邻结构.在人造数据、Wine数据和6个基因表达数据上进行实验,实验结果表明该算法优于其他降维方法.
Neighborhood and sparsity structure preserving projections have been widely used in dimensionality reduction,but most of them consider single structures. Moreover, existing nonlinear DR methods can not get an accurate projection function, which limits their applications. To overcome these problems, we propose a nonlinear dimensionality reduction method SNP-ELM by extending the extreme learning machine model. SNP-ELM is a nonlinear unsupervised dimensionlity reduction method, which takes both sparsity structure and neighborhood structure into account. The experimental results on toy data, wine data and six gene expression data show that our method significantly outperforms the compared dimensionality reduction methods.
引文
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