Central Subspace Dimensionality Reduction Using Covariance Operators

By Minyoung Kim, Vladimir I. Pavlovic. 2011

24 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. David Balduzzi
2. Mengjie Zhang
3. Muhammad Ghifary
4. W. Kleijn
5. Alberto Domingo Pascual-Montano
6. Javier Vargas
7. Wouter M. Kouw
8. Carlos Oscar Sánchez Sorzano

In Statistics and Probability

1. Alberto Domingo Pascual-Montano
2. W. Kleijn
3. Mengjie Zhang
4. Javier Vargas
5. David Balduzzi
6. Muhammad Ghifary
7. Wouter M. Kouw
8. Carlos Oscar Sánchez Sorzano

Citations in the corpus

• Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization (2015, background): “DICA extends Kernel PCA [26] by incorporating the distributional variance to reduce the dissimilarity across domains and the central subspace [60] to capture the functional relationship between the features and their corresponding labels.”
• An introduction to domain adaptation and transfer learning (2018, background): “duce class overlap. The functional relationship between the input and the classes can be preserved by incorporating a central subspace in which the input and the classes are conditionally independent [92,121]. Maintaining this central subspace during optimization ensures that classes remain separabl”
• A survey of dimensionality reduction techniques (2014, background): “Many other works followed since then, for the most recent works and for a bibliographical review see [Bian2011, Cai2011, Kim2011, Lin2011, Batmanghelich2012].”