← Back to papers
2008 NIPS 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.

🧭 Keyword Pioneer — sliced inverse regression
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing
🐣 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

Keywords

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

Related papers

On the Efficient Minimization of Classification Calibrated Surrogates 2008
Hebbian Learning of Bayes Optimal Decisions 2008
Biasing Approximate Dynamic Programming with a Lower Discount Factor 2008
Counting Solution Clusters in Graph Coloring Problems Using Belief Propagation 2008
Domain Adaptation with Multiple Sources 2008