Reliable eigenspectra for new generation surveys

Budavàri, Tamás; Wild, Vivienne; Szalay, Alexander S.; Dobos, László; Yip, Ching-Wa

doi:10.1111/j.1365-2966.2009.14415.x

Cited by 31 publications

(48 citation statements)

References 21 publications

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“…Thanks for helpful discussions from Du Bing toward the flux re-calibration and drawing Figure 4. And thanks Budavári et al for the helps about their code in Budavári et al (2009). This work is supported by the National Key Basic Research Program of China (Grant No.…”

Section: Acknowledgementsmentioning

confidence: 97%

Spectral classification and composites of galaxies in LAMOST DR4

Wang

Luo

Shen

et al. 2017

Monthly Notices of the Royal Astronomical Society

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We study the classification and composite spectra of galaxy in the fourth data release (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). We select 40,182 spectra of galaxies from LAMOST DR4, which have photometric information but no spectroscopic observations in the Sloan Digital Sky Survey(SDSS). These newly observed spectra are re-calibrated and classified into six classes, i.e. passive, Hα-weak, star-forming, composite, LINER and Seyfert using the line intensity (Hβ, [OIII]λ5007, Hα and [NII]λ6585). We also study the correlation between spectral classes and morphological types through three parameters: concentration index, (ur) color, and D4000 n index. We calculate composite spectra of high signal-to-noise ratio(S/N) for six spectral classes, and using these composites we pick out some features that can differentiate the classes effectively, including Hβ, Fe5015, Hγ A , HK, and Mg 2 band etc. In addition, we compare our composite spectra with the SDSS ones and analyse their difference. A galaxy catalogue of 40,182 newly observed spectra (36,601 targets) and the composite spectra of the six classes are available online.

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Section: Acknowledgementsmentioning

confidence: 97%

Spectral classification and composites of galaxies in LAMOST DR4

Wang

Luo

Shen

et al. 2017

Monthly Notices of the Royal Astronomical Society

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“…In astronomy, for example, PCA and the SVD have been used directly for spectral classification [32,33,34,35], to predict morphological types using galaxy spectra [36], to select quasar candidates from sky surveys [37], etc. [38,39,40,41]. Size is an issue, but so too is understanding the data [42,43]; and many of these studies have found that principal components of galaxy spectra (and their elements) correlate with various physical processes such as star formation (via absorption and emission line strengths of, e.g., the so-called Hα spectral line) as well as with galaxy color and morphology.…”

Section: Motivating Scientific Applicationsmentioning

confidence: 99%

Randomized Algorithms for Matrices and Data

Mahoney¹

2012

Chapman &Amp; Hall/CRC Data Mining and Knowledge Discovery Series

334

373

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Randomized algorithms for very large matrix problems have received a great deal of attention in recent years. Much of this work was motivated by problems in large-scale data analysis, largely since matrices are popular structures with which to model data drawn from a wide range of application domains, and this work was performed by individuals from many different research communities. While the most obvious benefit of randomization is that it can lead to faster algorithms, either in worst-case asymptotic theory and/or numerical implementation, there are numerous other benefits that are at least as important. For example, the use of randomization can lead to simpler algorithms that are easier to analyze or reason about when applied in counterintuitive settings; it can lead to algorithms with more interpretable output, which is of interest in applications where analyst time rather than just computational time is of interest; it can lead implicitly to regularization and more robust output; and randomized algorithms can often be organized to exploit modern computational architectures better than classical numerical methods.This monograph will provide a detailed overview of recent work on the theory of randomized matrix algorithms as well as the application of those ideas to the solution of practical problems in large-scale data analysis. Throughout this review, an emphasis will be placed on a few simple core ideas that underlie not only recent theoretical advances but also the usefulness of these tools in large-scale data applications. Crucial in this context is the connection with the concept of statistical leverage. This concept has long been used in statistical regression diagnostics to identify outliers; and it has recently proved crucial in the development of improved worst-case matrix algorithms that are also amenable to high-quality numerical implementation and that are useful to domain scientists. This connection arises naturally when one explicitly decouples the effect of randomization in these matrix algorithms from the underlying linear algebraic structure. This decoupling also permits much finer control in the application of randomization, as well as the easier exploitation of domain knowledge.Most of the review will focus on random sampling algorithms and random projection algorithms for versions of the linear least-squares problem and the low-rank matrix approximation problem. These two problems are fundamental in theory and ubiquitous in practice. Randomized methods solve these problems by constructing and operating on a randomized sketch of the input matrix Afor random sampling methods, the sketch consists of a small number of carefully-sampled and rescaled columns/rows of A, while for random projection methods, the sketch consists of a small number of linear combinations of the columns/rows of A. Depending on the specifics of the situation, when compared with the best previously-existing deterministic algorithms, the resulting randomized algorithms have worst-case running time that is asympt...

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“…One way is to adapt streaming algorithms (Weng et al, 2003; Zhao et al, 2006; Budavari et al, 2009). These algorithms usually require only one pass through the data matrix Ỹ during which information about the first k singular vectors is accumulated sequentially.…”

Section: Al Large Sample Sizementioning

confidence: 99%

Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis

Zipunnikov¹,

Greven²,

Shou³

et al. 2014

Ann. Appl. Stat.

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We develop a flexible framework for modeling high-dimensional imaging data observed longitudinally. The approach decomposes the observed variability of repeatedly measured high-dimensional observations into three additive components: a subject-specific imaging random intercept that quantifies the cross-sectional variability, a subject-specific imaging slope that quantifies the dynamic irreversible deformation over multiple realizations, and a subject-visit specific imaging deviation that quantifies exchangeable effects between visits. The proposed method is very fast, scalable to studies including ultra-high dimensional data, and can easily be adapted to and executed on modest computing infrastructures. The method is applied to the longitudinal analysis of diffusion tensor imaging (DTI) data of the corpus callosum of multiple sclerosis (MS) subjects. The study includes 176 subjects observed at 466 visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 30,000 voxels.

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Reliable eigenspectra for new generation surveys

Cited by 31 publications

References 21 publications

Spectral classification and composites of galaxies in LAMOST DR4

Spectral classification and composites of galaxies in LAMOST DR4

Randomized Algorithms for Matrices and Data

Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis

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