Boosted Whittaker-Henderson Graduation of Order 1: A Graph Spectral Filter Using Discrete Cosine Transform

The Whittaker-Henderson (WH) graduation of order 1 is a smoothing/filtering method for equally spaced one-dimensional data. Inspired by Phillips and Shi, this paper introduces the boosted version of the WH graduation of order 1. We show that it is a graph spectral filter using the discrete cosine transform, and then provide a simple formula for the (i, j) entry of its smoother matrix. We also show that it is a linear smoother such that the filter weights sum to unity and the smoother matrix is bisymmetric, i.e., symmetric and centrosymmetric. GNU Octave user-defined functions based on the obtained results are also provided.

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Publication Details

Journal
Contemporary Mathematics
Published
2025-03-20
DOI
https://doi.org/10.37256/cm.6220254409
Citations
3
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
4.96
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article

Boosted Whittaker-Henderson Graduation of Order 1: A Graph Spectral Filter Using Discrete Cosine Transform

Hiroshi Yamada, Ruoyi Bao
3 citations
Contemporary Mathematics
Neural Networks and Applications
4.96
article

Boosted Whittaker-Henderson Graduation of Order 1: A Graph Spectral Filter Using Discrete Cosine Transform

Hiroshi Yamada, Ruoyi Bao
article en
3 citations

Abstract

The Whittaker-Henderson (WH) graduation of order 1 is a smoothing/filtering method for equally spaced one-dimensional data. Inspired by Phillips and Shi, this paper introduces the boosted version of the WH graduation of order 1. We show that it is a graph spectral filter using the discrete cosine transform, and then provide a simple formula for the (i, j) entry of its smoother matrix. We also show that it is a linear smoother such that the filter weights sum to unity and the smoother matrix is bisymmetric, i.e., symmetric and centrosymmetric. GNU Octave user-defined functions based on the obtained results are also provided.

Contemporary Mathematics
Hiroshima University (JP)
Openalex Percentile: Top 6%
Neural Networks and Applications
4.96
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