摘要
现有非线性维数约简算法均需要人工设定适当的邻域点数k(或者邻域半ε)才能获得合理的嵌入结果．但常用的基于嵌入残差的邻域参数选择方法本质上是循环依赖的,不能有效工作．为实现非线性维数约简算法的定量评价的参数辨识,从讨论优化嵌入的基本判定原则出发,给出了基于空域互信息和正则依赖指数谱的优化嵌入判据实现嵌入质量的定量评价和非线性维数约简算法的非监督参数辨识．仿真实验表明,直观的嵌入质量可被优化嵌入判据有效反映,且由嵌入集拟合恢复原数据集时的拟合精度与优化嵌入判据之间存在显著的正相关．
Popular nonlinear dimensionality reduction algorithms, e.g. LLE, Isomap and SIE, must configure neighborhood parameters in advance to gain meaningful embedding results. But current criteria of neighborhood parameters selection based on embedding residual are not independent of neighborhood parameters. Therefore it cannot work universally. To improve the availability of nonlinear dimensionality reduction algorithms in the field of self-adaptive manifold learning, the optimal embedding principles are discussed, and criteria of optimal embedding based on spatial mutual information and normalized dependency index spectrum are proposed to quantitatively evaluate embedding quality and achieve unsupervised parameters identifications. Simulation shows that intuitive embedding quality can be effectively indexed by proposed criteria, and there is a remarkably positive correlation between fitting precisions of embedding sets and criteria of optimal embedding.
出处
《天津大学学报》
EI
CAS
CSCD
北大核心
2007年第1期28-34,共7页
Journal of Tianjin University(Science and Technology)
基金
天津市科技发展计划资助项目(04310941R)
天津市自然科学基金应用基础研究资助项目(05YFJMJCll700)
国家自然科学基金资助项目(60603027).
关键词
流形学习
非线性维数约简
空域互信息
正规依赖指数谱
自组织等距嵌入
优化嵌入判据
manifold learning
nonlinear dimensionality reduction
spatial mutual information
normalized dependency index spectrum
self-organizing isometric embedding
criteria of optimal embedding
