Kernel Sliced Inverse Regression with Applications to Classification
By Han-Ming Wu. 2008
141 citations recorded by the source corpus.
Subfields: Artificial Intelligence, 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 Artificial Intelligence
- Krikamol Muandet
- Bernhard Schölkopf
- David Balduzzi
- David Lopez-Paz
- Kenji Fukumizu
- Vladimir I. Pavlovic
- Minyoung Kim
- Bharath K. Sriperumbudur
In Computer Vision and Pattern Recognition
- Krikamol Muandet
- Bernhard Schölkopf
- David Balduzzi
- Vladimir I. Pavlovic
- Minyoung Kim
- Dacheng Tao
- Luc Van Gool
- Ming-Hsuan Yang
In Statistics and Probability
- Krikamol Muandet
- Bernhard Schölkopf
- David Balduzzi
- Vladimir I. Pavlovic
- Minyoung Kim
- Kenji Fukumizu
- Bharath K. Sriperumbudur
- Arthur Gretton
Citations in the corpus
- Domain Generalization via Invariant Feature Representation (2013, methodology): “To avoid slicing the output space explicitly (Li 1991, Wu 2008), we exploit its kernel structure when estimating the covariance of the inverse regressor.”
- Central Subspace Dimensionality Reduction Using Covariance Operators (2011, methodology): “The data set called curves was devised for testing KSIR in [31], where it is generated by: y 1⁄4 signðb1 xþ 1Þ logðjb2 xþ a0 þ 2jÞ for some b1;b2 2 IR(15), x Nð0; I15Þ, 1; 2 Nð0; 1Þ, and a constant a0.”