基于流形学习的图像降维算法研究  

Study of Image Dimensional Reduction Algorithm Based on Manifold Learning

作　　者：侯远韶 

机构地区：[1]郑州工业应用技术学院信息工程学院,河南郑州451100

出　　处：《洛阳理工学院学报（自然科学版）》2015年第4期59-62,89,共5页Journal of Luoyang Institute of Science and Technology：Natural Science Edition

基　　金：国家自然科学基金:基于二维随机映射和一范数优化的有监督图像分类研究(6115200)

摘　　要：传统的一维、二维图像特征提取忽略了图像的结构信息,由此带来识别精度的损失;三维和多维图像的特征提取虽然考虑了数据结构之间的彼此联系,但却带来了维数灾难,增加了计算复杂度。本文利用流形学习,在原始的数据空间中嵌入稳定的流形,从而使多维数据中的特征数据映射到流形上,发现隐含在高维数据集中人们无法感知的低维结构,在不丢失数据信息的前提下,降低原始数据的维数,从而降低计算复杂度。One-dimensional and two-dimensional image feature extraction ignores the image structure information,which will result in the loss of recognition accuracy. Three-dimensional and multi-dimensional image feature extraction considers the links between each data structure,however it brings the curse of dimensionality,which will increase the computational complexity. Accordingly,in this paper,manifold learning is taken to embed a stable manifold in the original data space,thus the multi-dimensional data feature data can be mapped to the manifold,the invisible low-dimensional structure implicated in the high-dimensional data sets can be perceived,the original data dimension can be reduced without losing data,and thereby the computational complexity can be reduced.

关 键 词：流形学习 特征提取 本征维数 数据挖掘 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]