Supervised Learning on Local Tangent Space

摘要

A novel supervised learning method is proposed in this paper. It is an extension of local tangent space alignment (LTSA) to supervised feature extraction. First LTSA has been improved to be suitable in a changing, dynamic environment, that is, now it can map new data to the embedded low-dimensional space. Next class membership information is introduced to construct local tangent space when data sets contain multiple classes. This method has been applied to a number of data sets for classification and performs well when combined with some simple classifiers.