Wavelet Methods in Data Mining

Lin, En-Bing

doi:10.1007/0-387-25465-x_27

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…As we did not have any reference methods of feature extraction from Be stars for comparison, we compared our results with a common method of feature extraction from time series using wavelets, in which N largest coefficients of wavelet transform are kept and the rest of the coefficients are set to zero [20] . In experiments, N = 10 was used.…”

Section: Feature Extractionmentioning

confidence: 99%

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Bromová

Škoda

Vážný

2014

Int. J. Autom. Comput.

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Abstract:Advances in the technology of astronomical spectra acquisition have resulted in an enormous amount of data available in world-wide telescope archives. It is no longer feasible to analyze them using classical approaches, so a new astronomical discipline, astroinformatics, has emerged. We describe the initial experiments in the investigation of spectral line profiles of emission line stars using machine learning with attempt to automatically identify Be and B[e] stars spectra in large archives and classify their types in an automatic manner. Due to the size of spectra collections, the dimension reduction techniques based on wavelet transformation are studied as well. The result clearly justifies that machine learning is able to distinguish different shapes of line profiles even after drastic dimension reduction.

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Section: Feature Extractionmentioning

confidence: 99%

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Bromová

Škoda

Vážný

2014

Int. J. Autom. Comput.

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Spatial pattern discovery for hyperspectral images based on multiresolution analysis

Beauchemin

2012

International Journal of Image and Data Fusion

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Automatic Kalman-filter-based wavelet shrinkage denoising of 1D stellar spectra

Gilda

Slepian

2019

Monthly Notices of the Royal Astronomical Society

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We propose a non-parametric method to denoise 1D stellar spectra based on wavelet shrinkage followed by adaptive Kalman thresholding. Wavelet shrinkage denoising involves applying the Discrete Wavelet Transform (DWT) to the input signal, 'shrinking' certain frequency components in the transform domain, and then applying inverse DWT to the reduced components. The performance of this procedure is influenced by the choice of base wavelet, the number of decomposition levels, and the thresholding function. Typically, these parameters are chosen by 'trial and error', which can be strongly dependent on the properties of the data being denoised. We here introduce an adaptive Kalman-filter-based thresholding method that eliminates the need for choosing the number of decomposition levels. We use the 'Haar' wavelet basis, which we found to be the best-suited for 1D stellar spectra. We introduce various levels of Poisson noise into synthetic PHOENIX spectra, and test the performance of several common denoising methods against our own. It proves superior in terms of noise suppression and peak shape preservation. We expect it may also be of use in automatically and accurately filtering low signal-to-noise galaxy and quasar spectra obtained from surveys such as SDSS, Gaia, LSST, PESSTO, VANDELS, LEGA-C, and DESI.

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Wavelet Methods in Data Mining

Cited by 5 publications

References 32 publications

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Spatial pattern discovery for hyperspectral images based on multiresolution analysis

Automatic Kalman-filter-based wavelet shrinkage denoising of 1D stellar spectra

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