Seeing is Learning in High Dimensions: The Synergy Between Dimensionality Reduction and Machine Learning
Abstract
References
Index Terms
- Seeing is Learning in High Dimensions: The Synergy Between Dimensionality Reduction and Machine Learning
Recommendations
Incremental Nonlinear Dimensionality Reduction by Manifold Learning
Understanding the structure of multidimensional patterns, especially in unsupervised cases, is of fundamental importance in data mining, pattern recognition, and machine learning. Several algorithms have been proposed to analyze the structure of high-...
Nonparametric discriminant multi-manifold learning for dimensionality reduction
Based on that data sampled from the same class locate on one manifold and those labeled different classes reside on the corresponding manifolds, traditional data classification problem can be reasoned to multiply manifolds identification. Thus in this ...
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
Abstract--We propose an eigenvector-based heteroscedastic linear dimension reduction (LDR) technique for multiclass data. The technique is based on a heteroscedastic two-class technique which utilizes the so-called Chernoff criterion, and successfully ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0