Nonparametric independent component analysis

By A. Samarov, Alexandre B. Tsybakov. 2004

48 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Statistics and Probability, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3fef82efdfd851f0892d2c9c97f47c6ec1427cd6

How researchers see this paper

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

In Analytical Chemistry

1. Bernhard Schölkopf
2. Peter J. Bickel
3. Olivier Bousquet
4. Arthur Gretton
5. Ralf Herbrich
6. Aiyou Chen
7. Alex Smola
8. Kenji Fukumizu

In Statistics and Probability

1. Arthur Gretton
2. Bernhard Schölkopf
3. Alex Smola
4. Kenji Fukumizu
5. Bharath K. Sriperumbudur
6. Jan Peters
7. Dino Sejdinovic
8. Le Song

In Signal Processing

1. Arthur Gretton
2. Bernhard Schölkopf
3. Alex Smola
4. Kenji Fukumizu
5. Bharath K. Sriperumbudur
6. Dominik Janzing
7. Peter J. Bickel
8. Olivier Bousquet

Citations in the corpus

• Measuring Statistical Dependence with Hilbert-Schmidt Norms (2005, methodology): “We acknowledge that the application of a general dependence function to linear ICA is not an optimal non-parametric approach to the problem of estimating the entries in A, as discussed in [18].”
• Efficient independent component analysis (2006, background): “Samarov and Tsybakov [22] proposed and analyzed a √ n-consistent estimate of W under mild conditions.”
• Kernel Methods for Measuring Independence (2005, methodology): “We acknowledge that the application of a general dependence function to linear ICA is not guaranteed to be an optimal non-parametric approach to the problem of estimating the entries in B—for instance, Samarov and Tsybakov (2004) provide a method that guarantees √ n-consistent estimates of the colum”