2009
NeurIPS
NeurIPS 2009
Manifold Regularization for SIR with Rate Root-n Convergence
Abstract
In this paper, we study the manifold regularization for the Sliced Inverse Regression (SIR). The manifold regularization improves the standard SIR in two aspects: 1) it encodes the local geometry for SIR and 2) it enables SIR to deal with transductive and semi-supervised learning problems. We prove that the proposed graph Laplacian based regularization is convergent at rate root-n. The projection directions of the regularized SIR are optimized by using a conjugate gradient method on the Grassmann manifold. Experimental results support our theory.
📈
Trend Setter
- Semi-Supervised Learning
🧭
Keyword Pioneer
- grassmann manifold
🐣
Hot Topic Early Bird
- dimensionality reduction
Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Learning Types > Semi-Supervised Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Machine Learning > Core Methods > Dimensionality Reduction
Artificial Intelligence > Learning Paradigms > Semi-Supervised Learning
Machine Learning > Core Methods > Feature Extraction