空间光滑且完整的子空间学习算法

Spatially Smooth and Complete Subspace Learning Algorithm

提出一种空间光滑且完整的子空间学习算法.它融合了主成分分析、空间光滑的子空间学习算法和局部敏感判别投影的技术特点.不但保持了数据流形的全局和局部几何结构,而且保持了它的判别信息和空间关系.从原始样本提取全局和局部特征经线性变换组成新样本,再从新样本中提取最佳分类特征,最后由分类器完成分类识别.同一般的子空间算法相比,该算法提高了识别率.实验结果验证了该算法的有效性.

A spatially smooth and complete subspace learning algorithm is proposed for feature extraction and recognition. Based on principle component analysis, spatially smooth subspace learning and locally sensitive discriminant analysis, the proposed algorithm preserves globally and locally geometrical structure and information of discrimination and spatial correlation. The globally geometrical features and locally spatial correlation information are extracted from original data samples, and then they are linearly transformed into new data samples. Subsequently, the best features are extracted for classification. Compared with general subspace learning algorithms, the proposed algorithm improves the recognition rate. Experimental results demonstrate the effectiveness of the proposed algorithm.