Fetching the paper…

Reading the bibliography…

2011

Selecting the rank of truncated SVD by Maximum Approximation Capacity

Frank, Mario, Buhmann, Joachim M.

Original
PDF
Post on X

Understand

Truncated Singular Value Decomposition (SVD) calculates the closest rank-$k$ approximation of a given input matrix.

Selecting the appropriate rank $k$ defines a critical model order choice in most applications of SVD.
To obtain a principled cut-off criterion for the spectrum, we convert the underlying optimization problem into a noisy channel coding problem.
The optimal approximation capacity of this channel controls the appropriate strength of regularization to suppress noise.

Selecting the rank of truncated SVD by Maximum Approximation Capacity · Around

Built on

G. Schwarz, “Estimating the dimension of a model,” The Annals of Statistics , vol. 6, no. 2, pp. 461–464, 1978
1978
Earlier work this paper cites.
T. P. Minka, “Automatic choice of dimensionality for PCA,” in NIPS , 2000, p. 514
2000
Earlier work this paper cites.

Similar

J. M. Buhmann, “Information theoretic model validation for clustering,” in ISIT , 2010
2010
Cited alongside, same era.
J. M. Buhmann, M. H. Chehreghani, M. Frank, and A. P. Streich, “Information theoretic model selection for pattern analysis,” in JMLR: Workshop and Conference Proceedings 7, 1-8 , 2011
2011
Cited alongside, same era.

Then

M. Frank, M. H. Chehreghani, and J. M. Buhmann, “The minimum transfer cost principle for model-order selection,” in ECML , 2011
2011
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…