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2008 NeurIPS NeurIPS 2008

Localized Sliced Inverse Regression

Abstract

We developed localized sliced inverse regression for supervised dimension reduction. It has the advantages of preventing degeneracy, increasing estimation accuracy, and automatic subclass discovery in classification problems. A semisupervised version is proposed for the use of unlabeled data. The utility is illustrated on simulated as well as real data sets.

📈 Trend Setter - Regression

🐣 Hot Topic Early Bird - semi-supervised learning

Authors

Qiang Wu, Sayan Mukherjee, Feng Liang

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Representation Learning Machine Learning > Learning Types > Semi-Supervised Learning Machine Learning > Optimization & Theory > Statistical Learning Machine Learning > Core Methods > Dimensionality Reduction Machine Learning > Learning Types > Supervised Learning Artificial Intelligence > Learning Paradigms > Semi-Supervised Learning Deep Learning > Learning Types > Regression

Keywords

semi-supervised learning dimension reduction sliced inverse regression subclass discovery supervised dimension reduction classification problem

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