Abstract 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.
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2009, Vol. 22 
