Semi-Supervised Regression via Local Block Coordinate
Gelan Yang, Xue Xu, Huixia Jin · 2009
In many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms have attracted much attention. In this paper, semi-supervised regression via local block coordinate algorithm is proposed. This algorithm preserves more geometrical knowledge of the high-dimensional data by local tangent space alignment, we take the method of automatic alignment of local representations to realize preserving the linear projection between every local coordinate and the global coordinate. Experiments show that method can effectively exploit unlabeled data to improve regression estimates.