The PCA Lens-Finder: application to CFHTLS
D. ParaficzF. CourbinA. TramacereR. JosephR. B. MetcalfJean‐Paul KneibP. DubathDavid DrozFélicien FilleulDamien RingeisenChristoph Schäfer
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We present the results of a new search for galaxy-scale strong lensing systems in CFHTLS Wide. Our lens-finding technique involves a preselection of potential lens galaxies, applying simple cuts in size and magnitude. We then perform a Principal Component Analysis of the galaxy images, ensuring a clean removal of the light profile. Lensed features are searched for in the residual images using the clustering topometric algorithm DBSCAN. We find 1098 lens candidates that we inspect visually, leading to a cleaned sample of 109 new lens candidates. Using realistic image simulations we estimate the completeness of our sample and show that it is independent of source surface brightness, Einstein ring size (image separation) or lens redshift. We compare the properties of our sample to previous lens searches in CFHTLS. Including the present search, the total number of lenses found in CFHTLS amounts to 678, which corresponds to ~4 lenses per square degree down to i=24.8. This is equivalent to ~ 60.000 lenses in total in a survey as wide as Euclid, but at the CFHTLS resolution and depth.Keywords:
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The use of principal component analysis (PCA), also known as singular value decomposition (SVD), is a powerful tool that is frequently applied to the classification of hyperspectral images in remote sensing. Unfortunately, the utility of the resulting PCA may depend on the resolution of the original image, i.e., too coarse-grained of an image may result in inaccurate major principal components. This work presents an example of how the major principal component obtained from the PCA of a low-resolution image may be refined to obtain a more accurate estimate of the major principal component. The more accurate estimate is obtained by recursively performing a PCA on only those pixels that contribute strongly to the major principal component.
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The article discusses selected problems related to both principal component analysis (PCA) and factor analysis (FA). In particular, both types of analysis were compared. A vector interpretation for both PCA and FA has also been proposed. The problem of determining the number of principal components in PCA and factors in FA was discussed in detail. A new criterion for determining the number of factors and principal components is discussed, which will allow to present most of the variance of each of the analyzed primary variables. An efficient algorithm for determining the number of factors in FA, which complies with this criterion, was also proposed. This algorithm was adapted to find the number of principal components in PCA. It was also proposed to modify the PCA algorithm using a new method of determining the number of principal components. The obtained results were discussed.
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To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.
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Principal component analysis is the well-known method in pattern recognition, but classical principal component analysis extract some features that keep maximal scatter and the algorithm doesn't use the classificatory information of samples. Therefore, extracted features aren't very efficient to classification based on classical principal component analysis. Based on the image retrieve principle, the paper presents a kind of retrieve space principal component analysis (RS-PCA). Then, a supervised retrieve space principal component analysis (SRS-PCA) using classificatory information are developed according to RS-PCA. The algorithm makes the extracted features more effective and the recognition precision is increased. The experiments resulted on ORL and Yale face database demonstrate that the proposed algorithm has more powerful and excellent performance than classical principal component analysis.
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With the aim of establishing a rapid method to discriminate Boletus tomentipes samples from different regions, FTIR spectroscopy with the aid of principal component analysis and clustering analysis were used in the present study. The information of infrared spectra of B. tomentipes samples originated from 15 regions has been collected. The original infrared spectra was pretreated by multiplicative signal correction (MSC) in combination with second derivative and Norris smooth. The spectral data were analyzed by principal component analysis and cluster analysis after the optimal pretreatment of MSC+SD+ND (15, 5), and the reasons for the differences of B. tomentipes samples from different regions could be explained through the principal component loading plot. The results showed that, the RSDs of repeatability, accuracy and stability of the method were 0.17%, 0.08% and 0.27%, respectively, which indicated the method was stable and reliable. The cumulative contribution of first three principal components of PCA was 87.24% which could reflect the most information of the samples. Principal component scores scatter plot displaying the samples from same origin could clustered together and samples from different areas distributed in a relatively independent space. Which can distinguish samples collected from different origins, effectively. The loading plot of principal component showed that with the principal component contribution rate decreasing, the captured sample information of principal component was also reducing. In the wave number of 3 571, 2 958, 1 625, 1 456, 1 405, 1 340, 1 191, 1 143, 1 084, 935, 840, 727 cm-1, the first principal component captured a large amount of sample information which attributed to carbohydrates, proteins, amino acids, fat, fiber and other chemical substances. Which showed that the different contents of these chemical substances may be the basis of discrimination of B. tomentipes samples from different origins. Cluster analysis based on ward method and Euclidean distance has shown the classification and correlation among samples. Samples originated from 15 regions could be clustered correctly in accordance with the basic origins and the correct rate was 93.33%. Which can be used to identify and analyze B. tomentipes collected from different sites. Fourier transform infrared spectroscopy combined with principal component analysis and cluster analysis can be effectively used to discriminate origins of B. tomentipes mushrooms and the reasons for the differences of B. tomentipes samples from different regions could be explained. This method could provide a reliable basis for discrimination and application of wild edible mushrooms.
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ADVERTISEMENT RETURN TO ISSUEPREVArticleNEXTClassification of Vegetable Oils by Principal Component Analysis of FTIR SpectraDavid A. Rusak , Leah M. Brown , and Scott D. Martin View Author Information Department of Chemistry, University of Scranton, Scranton, PA 18510Cite this: J. Chem. Educ. 2003, 80, 5, 541Publication Date (Web):May 1, 2003Publication History Received3 August 2009Published online1 May 2003Published inissue 1 May 2003https://pubs.acs.org/doi/10.1021/ed080p541https://doi.org/10.1021/ed080p541research-articleACS PublicationsRequest reuse permissionsArticle Views2531Altmetric-Citations42LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Infrared light,Lipids,Mathematical methods,Plant derived food,Principal component analysis Get e-Alerts
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Principal component analysis (PCA) has been widely used for data dimension reduction and process fault detection. However, interpreting the principal components and the outcomes of PCA-based monitoring techniques is a challenging task since each principal component is a linear combination of the original variables which can be numerous in most modern applications. To address this challenge, we first propose the use of sparse principal component analysis (SPCA) where the loadings of some variables in principal components are restricted to zero. This paper then describes a technique to determine the number of non-zero loadings in each principal component. Furthermore, we compare the performance of PCA and SPCA in fault detection. The validity and potential of SPCA are demonstrated through simulated data and a comparative study with the benchmark Tennessee Eastman process.
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Optical imaging of the recently discovered infrared quasar IRAS 00275-2859 (Vader and Simon 1987) shows that it can be decomposed into two point sources embedded in an underlying extended image. The brighter point source is the quasar. The authors identify the extended image together with the fainter point source as the host galaxy system. An adjacent low-surface-brightness region to the northeast is also detected in B and R. Unlike the other recently discovered IRAS quasar (13349+2438), the QSO in IRAS 00275-2859 shows a UV excess typical of optically selected quasars. The B-R rest-frame color of the host-galaxy system corresponds to that of a late-type spiral galaxy. The low-surface-brightness region to the northeast has, to within the uncertainties, the same B-R color as that of the host-galaxy system. The authors propose that the quasar belongs to one of two interacting galaxies, that the second point source is the nucleus of the second galaxy, and that the low-surface-brightness region is the tidal signature of the interaction. The infrared spectral energy distribution of IRAS 00275-2859 suggests that the bulk of the infrared radiation is emitted by heated dust in the host-galaxy system.
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