Kernel Mean Estimation via Spectral FilteringKernel Mean Estimation via Spectral FilteringMuandet, Krikamol and Sriperumbudur, Bharath K. and Schölkopf, Bernhard2014

Paper summarynipsreviewsThe paper presents a family of kernel mean shrinkage estimators. These estimators generalize the ones proposed in \cite{journals/jmlr/FukumizuSG13} and can incoporate useful domain knowledge through spetral filters. Here is a summary of interesting contributions:
1. Theorem 1 that shows the consistency and admissibility of kmse presented in \cite{journals/jmlr/FukumizuSG13}.
2. The idea of spectral kmse (its use in this unsupervised setting) and similarity of final form with the supervised setting.
3. Theorem 5 that shows consistency of the proposed spectral kmse.

The paper presents a family of kernel mean shrinkage estimators. These estimators generalize the ones proposed in \cite{journals/jmlr/FukumizuSG13} and can incoporate useful domain knowledge through spetral filters. Here is a summary of interesting contributions:
1. Theorem 1 that shows the consistency and admissibility of kmse presented in \cite{journals/jmlr/FukumizuSG13}.
2. The idea of spectral kmse (its use in this unsupervised setting) and similarity of final form with the supervised setting.
3. Theorem 5 that shows consistency of the proposed spectral kmse.

Your comment:

You must log in before you can post this comment!

You must log in before you can submit this summary! Your draft will not be saved!

Preview:

0

Short Science allows researchers to publish paper summaries that are voted on and ranked! About