摘要
如何有效实现降维是现代成像光谱仪辨识地物类别的一个难点所在。该文在已知高光谱图像地物类别数的情况下,提出了一种采用混合最小描述长度(MMDL)模型选择准则确定高光谱图像本征维数的方法。该方法在期望最大化算法框架下同时实现混合PPCA降维和聚类,并根据MMDL准则确定数据降维维数,可以得到数据在概率意义下的精确的降维表征。仿真数据和真实数据进行的比较实验表明,该方法能精确地选择数据的本征维数。
An intrinsic dimensionality determination method for hyperspectral image with known class number is proposed, which is based on mixture model of probabilistic PCA. Different from common methods that determine the number of dimensionality reduction by setting the number or by eigenvalue thresholding, the algorithm simultaneously conducts dimensionality reduction and clustering under the frame of EM algorithm; and retrieves the intrinsic dimensionality according to the MMDL principle with probabilistically accurate reduced representation of the data. The method can achieve precise results applied to simulated data and real data.
出处
《计算机工程》
CAS
CSCD
北大核心
2007年第9期204-206,共3页
Computer Engineering
关键词
降维
本征维数
混合概率主成分分析
混合最小描述长度准则
期望最大化算法
Dimensionality reduction
Intrinsic dimensionality
Mixture of probabilistic principal component analysis （PPCA）
Principle of mixture minimum description length （MMDL）
Expectation maximization（EM） algorithm
