Semi-supervised Dimension Reduction with Kernel Sliced Inverse Regression

摘要

This study is an attempt to draw on research of semi-supervised dimension reduction. Many real world problems can be formulated as semi-supervised problems since the data labeling is much more challenging to obtain than the unlabeled data. Dimension reduction benefits the computation performance and is usually applied in the problem with high dimensional data. This paper proposes a semi-supervised dimension reduction achieved with the kernel sliced inverse regression (KSIR). The prior information is applied to estimate the statistical parameters in the KSIR formula. The semi-supervised KSIR performs comparably to other established methods but much more efficient.