Regularized Pre-image Estimation for Kernel PCA De-noising: Input Space Regularization and Sparse Reconstruction

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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Regularized Pre-image Estimation for Kernel PCA De-noising : Input Space Regularization and Sparse Reconstruction. / Abrahamsen, Trine Julie; Hansen, Lars Kai.

In: Journal of Signal Processing Systems, Vol. 65, No. 3, 2011, p. 403-412.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

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@article{a591b3d9f9774b6e8bc65eb4d45849a1,
title = "Regularized Pre-image Estimation for Kernel PCA De-noising: Input Space Regularization and Sparse Reconstruction",
keywords = "De-noising, Regularization, Kernel PCA, Pre-image, Sparsity",
publisher = "Springer New York LLC",
author = "Abrahamsen, {Trine Julie} and Hansen, {Lars Kai}",
year = "2011",
doi = "10.1007/s11265-010-0515-4",
volume = "65",
number = "3",
pages = "403--412",
journal = "Journal of Signal Processing Systems",
issn = "1939-8018",

}

RIS

TY - JOUR

T1 - Regularized Pre-image Estimation for Kernel PCA De-noising

T2 - Input Space Regularization and Sparse Reconstruction

A1 - Abrahamsen,Trine Julie

A1 - Hansen,Lars Kai

AU - Abrahamsen,Trine Julie

AU - Hansen,Lars Kai

PB - Springer New York LLC

PY - 2011

Y1 - 2011

N2 - The main challenge in de-noising by kernel Principal Component Analysis (PCA) is the mapping of de-noised feature space points back into input space, also referred to as “the pre-image problem”. Since the feature space mapping is typically not bijective, pre-image estimation is inherently illposed. As a consequence the most widely used estimation schemes lack stability. A common way to stabilize such estimates is by augmenting the cost function by a suitable constraint on the solution values. For de-noising applications we here propose Tikhonov input space distance regularization as a stabilizer for pre-image estimation, or sparse reconstruction by Lasso regularization in cases where the main objective is to improve the visual simplicity. We perform extensive experiments on the USPS digit modeling problem to evaluate the stability of three widely used pre-image estimators. We show that the previous methods lack stability in the is non-linear regime, however, by applying our proposed input space distance regularizer the estimates are stabilized with a limited sacrifice in terms of de-noising efficiency. Furthermore, we show how sparse reconstruction can lead to improved visual quality of the estimated pre-image.

AB - The main challenge in de-noising by kernel Principal Component Analysis (PCA) is the mapping of de-noised feature space points back into input space, also referred to as “the pre-image problem”. Since the feature space mapping is typically not bijective, pre-image estimation is inherently illposed. As a consequence the most widely used estimation schemes lack stability. A common way to stabilize such estimates is by augmenting the cost function by a suitable constraint on the solution values. For de-noising applications we here propose Tikhonov input space distance regularization as a stabilizer for pre-image estimation, or sparse reconstruction by Lasso regularization in cases where the main objective is to improve the visual simplicity. We perform extensive experiments on the USPS digit modeling problem to evaluate the stability of three widely used pre-image estimators. We show that the previous methods lack stability in the is non-linear regime, however, by applying our proposed input space distance regularizer the estimates are stabilized with a limited sacrifice in terms of de-noising efficiency. Furthermore, we show how sparse reconstruction can lead to improved visual quality of the estimated pre-image.

KW - De-noising

KW - Regularization

KW - Kernel PCA

KW - Pre-image

KW - Sparsity

U2 - 10.1007/s11265-010-0515-4

DO - 10.1007/s11265-010-0515-4

JO - Journal of Signal Processing Systems

JF - Journal of Signal Processing Systems

SN - 1939-8018

IS - 3

VL - 65

SP - 403

EP - 412

ER -