Iterative Principal Component Analysis Research Articles

Combining reference-star and angular differential imaging for high-contrast imaging of extended sources

Context. High-contrast imaging (HCI) is a technique designed to observe faint signals near bright sources, such as exoplanets and circumstellar disks. The primary challenge in revealing the faint circumstellar signal near a star is the presence of quasi-static speckles, which can produce patterns on the science images that are as bright, or even brighter, than the signal of interest. Strategies such as angular differential imaging (ADI) or reference-star differential imaging (RDI) aim to provide a means of removing the quasi-static speckles in post-processing. Aims. In this paper, we present and discuss the adaptation of state-of-the-art algorithms, initially designed for ADI, to jointly leverage angular and reference-star differential imaging (ARDI) for the direct HCI of circumstellar disks. Methods. Using a collection of HCI datasets, we assessed the performance of ARDI in comparison to ADI and RDI based on iterative principal component analysis (IPCA). These diverse datasets were acquired under various observing conditions and include the injection of synthetic disk models at various contrast levels. We also considered reference stars with different levels of correlation with the science targets. Results. Our results demonstrate that ARDI with IPCA improves the quality of recovered disk images and the sensitivity to planets embedded in disks, compared to ADI or RDI individually. This enhancement is particularly pronounced when dealing with extended sources exhibiting highly ambiguous structures that cannot be accurately retrieved using ADI alone, and when the quality of the reference frames is suboptimal, leading to an underperformance of RDI. We finally applied our method to a sample of real observations of protoplanetary disks taken in star-hopping mode, and propose to revisit the protoplanetary claims associated with these disks. Among eight proposed protoplanets claimed through velocity kinks or direct imaging, none of them were re-detected in our new processed images.

Connected Community Classification (C3): Development, Validation, and Geospatial Application for Population Health Promotion and Equity.

Social determinants of health (SDOH) impact population health. Leveraging community-level strengths related to SDOH through a social infrastructure perspective can optimize health behaviors and health outcomes to promote health equity. Our aims were to develop, validate, and apply the Connected Community Classification (C3) as comprehensive community-level measure of protective SDOH and structural factors in the Four Corners states region of the United States. C3 was developed using an iterative principal component analysis of publicly available data mapped to 5 SDOH domains. Regional clustering of C3 by zip code tabulation area (ZCTA) was identified using spatial autocorrelation methods. In adjusted spatial autoregressive models, we analyzed the association of C3 with high-risk health behaviors and chronic disease prevalence using publicly available data for population-level estimates of fruit and vegetable intake, physical activity, obesity, smoking, alcohol use, coronary heart disease (CHD), diabetes, and cancer. C3 was found to be reliable and valid; a C3 value of 10 indicates communities with greater connection (high), while a value of 1 indicates communities with greater separation (low) to social infrastructure. Lower connection, as measured by C3, was significantly inversely associated with lower fruit and vegetable intake, lower physical activity, and higher rates of obesity, smoking, CHD, diabetes, and cancer. C3 was significantly positively associated with heavy alcohol use. These findings demonstrate that communities connected to social infrastructure have better population health outcomes. C3 captures protective community attributes and can be used in future applications to assist health researchers, practitioners, nonprofits, and policymakers to advance social connection and health equity in geographically diverse underserved regions.

Inverse-problem versus principal component analysis methods for angular differential imaging of circumstellar disks

Context. Circumstellar disk images have highlighted a wide variety of morphological features. Recovering disk images from high-contrast angular differential imaging (ADI) sequences is, however, generally affected by geometrical biases, leading to unreliable inferences of the morphology of extended disk features. Recently, two types of approaches have been proposed to recover more robust disk images from ADI sequences: iterative principal component analysis (I-PCA) and inverse problem (IP) approaches. Aims. We introduce mustard, a new IP-based algorithm specifically designed to address the problem of the flux invariant to rotation in ADI sequences – a limitation inherent to the ADI observing strategy – and discuss the advantages of IP approaches with respect to PCA-based algorithms. Methods. The mustard model relies on the addition of morphological priors on the disk and speckle field to a standard IP approach to tackle rotation-invariant signals in circumstellar disk images. We compared the performance of mustard, I-PCA, and standard PCA on a sample of high-contrast imaging data sets acquired in different observing conditions, after injecting a variety of synthetic disk models at different contrast levels. Results. Mustard significantly improves the recovery of rotation-invariant signals in disk images, especially for data sets obtained in good observing conditions. However, the mustard model inadequately handles unstable ADI data sets and provides shallower detection limits than PCA-based approaches. Conclusions. Mustard has the potential to deliver more robust disk images by introducing a prior to address the inherent ambiguity of ADI observations. However, the effectiveness of the prior is partly hindered by our limited knowledge of the morphological and temporal properties of the stellar speckle halo. In light of this limitation, we suggest that the algorithm could be improved by enforcing a data-driven prior based on a library of reference stars.

Tackling Skewness, Noise, and Broken Characters in Mathematical Expression Segmentation

Tackling Skewness, Noise, and Broken Characters in Mathematical Expression Segmentation

Volume atrophy in medial temporal cortex and verbal memory scores in American Indians: Data from the Strong Heart Study.

Distinguishing Alzheimer's disease (AD) patient subgroups may optimize positive clinical outcomes. Cortical atrophy is correlated with memory deficits, but these associations are understudied in American Indians. We collected imaging and cognition data in the Strong Heart Study (SHS), a cohort of 11 tribes across three regions. We processed 1.5T MRI using FreeSurfer and iterative principal component analysis. Linear mixed models estimated volumetric associations with diabetes. Over mean 7 years follow-up (N=818 age 65-89 years), overall volume loss was 0.5% per year. Significant losses associated with diabetes were especially strong in the right hemisphere. Annualized hippocampal, parahippocampal, entorhinal atrophy were worse for men, older age, diabetes, hypertension, stroke; and associated with both encoding and retrieval memory losses. Our findings suggest that diabetes is an important risk factor in American Indians for cortical atrophy and memory loss. Future research should examine opportunities for primary prevention in this underserved population.

Improved Estimation of O-B Bias and Standard Deviation by an RFI Restoration Method for AMSR-2 C-Band Observations over North America

Spaceborne microwave radiometer observations play vital roles in surface parameter retrievals and data assimilation, but widespread radio-frequency interference (RFI) signals in the C-band channel result in a lack of valuable data over large areas. Establishing repaired data based on existing observation information is crucial. In this study, Advanced Microwave Scanning Radiometer (AMSR)-2 C-band data affected by RFI were accurately repaired through the iterative principal component analysis (PCA) method in 2016 over the U.S. land area. The standard deviation (STD) and bias characteristics of the brightness temperature in the C-band vertical polarization channel were compared and analyzed before and after the restoration to verify the assimilation application prospect of the repaired data. Not only was the spatial continuity of the microwave imager observations significantly improved following restoration; the STD and bias of the observation minus background (OMB) of the restored data were basically consistent with those of the RFI-free data. The STD of OMB exhibited obvious seasonal variations, which were approximately 4.0 K from January to May and 3.0 K from June to December, whereas the biases were near zero in winter but negative (approximately −2.0 K) in summer. The surface type and terrain height also critically affected the STD and bias. The STD decreased with increasing terrain height, whereas the bias exhibited the opposite trend. The STD was largest in low-vegetation areas (4.0 K) but only approximately 2.0–3.0 K in pine forest and brush areas. These results show that the restored data have a high prospect for retrieval application and assimilation, and the STD and bias estimation results also provide a reference for land-based AMSR-2 data assimilation.

Effect of Radio Frequency Interference-Contaminated AMSR2 Signal Restoration on Soil Moisture Retrieval

Soil moisture is a key variable of the climate system. Microwave remote sensing has become an essential means of obtaining soil moisture because of the unique advantages of its all-day and all-weather observation capability. Theoretically, low-frequency C-band observations are highly suitable for soil moisture retrieval because of their high sensitivity to soil moisture at vegetation roots. However, the quality of C-band observations suffers from radio frequency interference (RFI) over the United States. This paper used the iterative principal component analysis (PCA) method to repair RFI-contaminated second-generation Advanced Microwave Scanning Radiometer (AMSR-2) C-band observations, and the results of soil moisture retrieval based on restored data were evaluated. It was found that RFI could lead to nonconvergence in the retrieval of a large amount of data, and the application of repaired data in retrieval could result in the recovery of more than 80% of nonconvergent data, especially in spring and autumn. The retrieval results based on restored data attained a satisfactory correlation with ERA5 reanalysis data and European Space Agency Climate Change Initiative (ESA-CCI) soil moisture data and suitably agreed with precipitation observation data. Soil moisture generally exhibited a gradual increase from west to north and east. This feature was weakened due to the influence of monsoons in the east in summer. The western side of the Cascade Mountains is the wettest area of the United States, with soil moisture exceeding 0.4 m3 m−3. The driest region of the United States is located between the Rockies and Cordillera Mountains, and the soil moisture value is lower than 0.1 m3 m−3.

Identification of errors in variables linear state space models using iterative principal component analysis

ABSTRACT Development of dynamic process models from input–output data, also known as system identification, is a well-studied problem. The classical approach to system identification assumes that the input variables are measured without error. However, in practice all data collected from evolving processes (inputs and outputs) will contain errors. System identification using noisy inputs and outputs is known as Errors-in-Variables (EIV) identification. Existing methods for EIV model identification assume that the error variances are known and/or assume the system order to be known. We propose a novel approach combining subspace-based identification and a dynamic iterative principal components approach for identifying EIV MIMO models. The proposed method simultaneously estimates process order, delay, model coefficients, and error variances using a rigorous theoretical basis. A benchmark simulation example from literature is used to exhibit the effectiveness of the proposed approach.

Identification of errors-in-variables ARX models using modified dynamic iterative PCA

Identification of autoregressive models with exogenous input (ARX) is a classical problem in system identification. This article considers the errors-in-variables (EIV) ARX model identification problem, where input measurements are also corrupted with noise. The recently proposed Dynamic Iterative Principal Components Analysis (DIPCA) technique solves the EIV identification problem but is only applicable to white measurement errors. We propose a novel identification algorithm based on a modified DIPCA approach for identifying the EIV-ARX model for single-input, single-output (SISO) systems where the output measurements are corrupted with coloured noise consistent with the ARX model. Most of the existing methods assume important parameters like input-output orders, delay, or noise-variances to be known. This work’s novelty lies in the joint estimation of error variances, process order, delay, and model parameters. The central idea used to obtain all these parameters in a theoretically rigorous manner is based on transforming the lagged measurements using the appropriate error covariance matrix, which is obtained using estimated error variances and model parameters. Simulation studies on two systems are presented to demonstrate the efficacy of the proposed algorithm.

Circularly Projected Common Factors for Grouped Data

To extract the common factors from grouped data, multilevel factor models have been put forward in the literature, and methods based on iterative principal component analysis (PCA) and canonical correlation analysis (CCA) have been proposed for estimation purpose. While iterative PCA requires iteration and is hence time-consuming, CCA can only deal with two groups of data. Herein, we develop two new methods to address these problems. We first extract the factors within groups and then project the estimated group factors into the space spanned by them in a circular manner. We propose two projection processes to estimate the common factors and determine the number of them. The new methods do not require iteration and are thus computationally efficient. They can estimate the common factors for multiple groups of data in a uniform way, regardless of whether the number of groups is large or small. They not only overcome the drawbacks of CCA but also nest the CCA method as a special case. Finally, we theoretically and numerically study the consistency properties of these new methods and apply them to studying international business cycles and the comovements of retail prices.

Trend clustering from COVID-19 tweets using graphical lasso-guided iterative principal component analysis

This article presents a method for trend clustering from tweets about coronavirus disease (COVID-19) to help us objectively review the past and make decisions about future countermeasures. We aim to avoid detecting usual trends based on seasonal events while detecting essential trends caused by the influence of COVID-19. To this aim, we regard daily changes in the frequencies of each word in tweets as time series signals and define time series signals with single peaks as target trends. To successfully cluster the target trends, we propose graphical lasso-guided iterative principal component analysis (GLIPCA). GLIPCA enables us to remove trends with indirect correlations generated by other essential trends. Moreover, GLIPCA overcomes the difficulty in the quantitative evaluation of the accuracy of trend clustering. Thus, GLIPCA’s parameters are easier to determine than those of other clustering methods. We conducted experiments using Japanese tweets about COVID-19 from March 8, 2020, to May 7, 2020. The results show that GLIPCA successfully distinguished trends before and after the declaration of a state of emergency on April 7, 2020. In addition, the results reveal the international argument about whether the Tokyo 2020 Summer Olympics should be held. The results suggest the tremendous social impact of the words and actions of Japanese celebrities. Furthermore, the results suggest that people’s attention moved from worry and fear of an unknown novel pneumonia to the need for medical care and a new lifestyle as well as the scientific characteristics of COVID-19.

Dynamic Iterative Principal Components Analysis for Closed-loop, Model Identification

Dynamic Iterative Principal Components Analysis for Closed-loop, Model Identification

Iterative principal component analysis method for improvised classification of breast cancer disease using blood sample analysis.

Breast cancer is the most common cancer in women occurring worldwide. Some of the procedures used to diagnose breast cancer are mammogram, breast ultrasound, biopsy, breast magnetic resonance imaging, and blood tests such as complete blood count. Detecting breast cancer at an early stage plays an important role in diagnostic and curative procedures. This paper aims to develop a predictive model for detecting the breast cancer using blood samples data containing age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin, and chemokine monocyte chemoattractant protein 1 (MCP-1).The two main challenges encountered in this process are identification of biomarkers and the precision of disease prediction accuracy. The proposed methodology employs principal component analysis in a peculiar approach followed by random forest tree prediction model to discriminate between healthy and breast cancer patients. This approach extracts high communalities, a linear combination of input attributes in a systematic procedure as principal axis elements. The iteratively extracted principal axis elements combined with minimum number of input attributes are able to predict the disease with higher accuracy of classification with increased sensitivity and specificity score. The results proved that the proposed approach generates a higher predictor performance than the previous reported results by opting relevant extracted principal axis elements and attributes that commend the classifier with increased performance measures.

Chunk-wise regularised PCA-based imputation of missing data

Standard multivariate techniques like Principal Component Analysis (PCA) are based on the eigendecomposition of a matrix and therefore require complete data sets. Recent comparative reviews of PCA algorithms for missing data showed the regularised iterative PCA algorithm (RPCA) to be effective. This paper presents two chunk-wise implementations of RPCA suitable for the imputation of “tall” data sets, that is, data sets with many observations. A “chunk” is a subset of the whole set of available observations. In particular, one implementation is suitable for distributed computation as it imputes each chunk independently. The other implementation, instead, is suitable for incremental computation, where the imputation of each new chunk is based on all the chunks analysed that far. The proposed procedures were compared to batch RPCA considering different data sets and missing data mechanisms. Experimental results showed that the distributed approach had similar performance to batch RPCA for data with entries missing completely at random. The incremental approach showed appreciable performance when the data is missing not completely at random, and the first analysed chunks contain sufficient information on the data structure.

Exploring systematic biases, rooting methods and morphological evidence to unravel the evolutionary history of the genus Ficus (Moraceae).

Despite many attempts in the Sanger sequencing era, the phylogeny of fig trees remains unresolved, which limits our ability to analyze the evolution of key traits that may have contributed to their evolutionary and ecological success. We used restriction-site-associated DNA sequencing (c. 420kb) and 102 morphological characters to elucidate the relationships between 70 species of Ficus. To increase phylogenetic information for higher-level relationships, we targeted conserved regions and assembled paired reads into long loci to enable the retrieval of homologous loci in outgroup genomes. We compared morphological and molecular results to highlight discrepancies and reveal possible inference bias. For the first time, we recovered a monophyletic subgenus Urostigma (stranglers) and a clade with all gynodioecious Ficus. However, we show, with a new approach based on iterative principal component analysis, that it is not (and will probably never be) possible to homogenize evolutionary rates and GC content for all taxa before phylogenetic inference. Four competing positions for the root of the molecular tree are possible. The placement of section Pharmacosycea as sister to other fig trees is not supported by morphological data and considered a result of a long-branch attraction artefact to the outgroups. Regarding morphological features and indirect evidence from the pollinator tree of life, the topology that divides Ficus into monoecious versus gynodioecious species appears most plausible. It seems most likely that the ancestor of fig trees was a freestanding tree and active pollination is inferred as the ancestral state, contrary to previous hypotheses. However, ambiguity remains on the ancestral breeding system. Despite morphological plasticity, we advocate restoring a central role to morphology in our understanding of the evolution of Ficus, as it can help detect systematic errors that appear more pronounced with larger molecular datasets.

Localized disaster risk management index for the Philippines: Is your municipality ready for the next disaster?

We developed a Localized Disaster Risk Management Index (DRMI) that measures how well local government units (LGUs) in the Philippines prepare for a disaster. Focusing on LGUs that have experienced at least one of the four hydrometeorological hazards, strong winds and rain, floods, landslide/mudslide, and big waves, we capture in one number the ex post and ex ante risk management strategies that influence post disaster outcomes. Given the nature of our data, we used the iterative principal component analysis to compute for the LGUs' Localized DRMI, which was then correlated with conditions, outcomes, and social indicators. Our results show a negative correlation between localized DRMI and recovery, which means that LGUs with high Localized DRMI scores are also those that have not fully recovered. This does not mean that these LGUs would be better off having lower scores. This result implies LGUs that perform well in terms of Localized DRMI scores are also those that frequently experience very severe disasters due to hydrometeorological hazards. Just as they are recovering from a disaster, another one hits them. This is corroborated by other correlation results: positive for severity and frequency, positive for poverty, and negative for LGUs’ revenue.

Robust normal vector estimation in 3D point clouds through iterative principal component analysis

This paper introduces a robust normal vector estimator for point cloud data. It can handle sharp features as well as smooth areas. Our method is based on the inclusion of a robust estimator into a Principal Component Analysis in the neighborhood of the studied point, so that it can detect and reject outliers automatically during the estimation. A projection process ensures robustness against noise. Two automatic initializations are computed, leading to independent optimizations making the algorithm robust to neighborhood anisotropy around sharp features. An evaluation has been carried out in which the algorithm is compared to state-of-the-art methods. The results show that it is more robust against low and/or non-uniform samplings, high noise levels and outliers. Moreover, our algorithm is fast relative to existing methods handling sharp features. The code is available on the website: https://projet.liris.cnrs.fr/pcr/, and integrated in the platform: https://github.com/MEPP-team/MEPP2.

ORIGIN: Blind detection of faint emission line galaxies in MUSE datacubes

Context. One of the major science cases of the Multi Unit Spectroscopic Explorer (MUSE) integral field spectrograph is the detection of Lyman-alpha emitters at high redshifts. The on-going and planned deep fields observations will allow for one large sample of these sources. An efficient tool to perform blind detection of faint emitters in MUSE datacubes is a prerequisite of such an endeavor. Aims. Several line detection algorithms exist but their performance during the deepest MUSE exposures is hard to quantify, in particular with respect to their actual false detection rate, or purity. The aim of this work is to design and validate an algorithm that efficiently detects faint spatial-spectral emission signatures, while allowing for a stable false detection rate over the data cube and providing in the same time an automated and reliable estimation of the purity. Methods. The algorithm implements (i) a nuisance removal part based on a continuum subtraction combining a discrete cosine transform and an iterative principal component analysis, (ii) a detection part based on the local maxima of generalized likelihood ratio test statistics obtained for a set of spatial-spectral profiles of emission line emitters and (iii) a purity estimation part, where the proportion of true emission lines is estimated from the data itself: the distribution of the local maxima in the “noise only” configuration is estimated from that of the local minima. Results. Results on simulated data cubes providing ground truth show that the method reaches its aims in terms of purity and completeness. When applied to the deep 30 h exposure MUSE datacube in the Hubble Ultra Deep Field, the algorithms allows for the confirmed detection of 133 intermediate redshifts galaxies and 248 Lyα emitters, including 86 sources with no Hubble Space Telescope counterpart. Conclusions. The algorithm fulfills its aims in terms of detection power and reliability. It is consequently implemented as a Python package whose code and documentation are available on GitHub and readthedocs.

A different view on fine-scale population structure in Western African populations

Due to its long genetic evolutionary history, Africans exhibit more genetic variation than any other population in the world. Their genetic diversity further lends itself to subdivisions of Africans into groups of individuals with a genetic similarity of varying degrees of granularity. It remains challenging to detect fine-scale structure in a computationally efficient and meaningful way. In this paper, we present a proof-of-concept of a novel fine-scale population structure detection tool with Western African samples. These samples consist of 1396 individuals from 25 ethnic groups (two groups are African American descendants). The strategy is based on a recently developed tool called IPCAPS. IPCAPS, or Iterative Pruning to CApture Population Structure, is a genetic divisive clustering strategy that enhances iterative pruning PCA, is robust to outliers and does not require a priori computation of haplotypes. Our strategy identified in total 12 groups and 6 groups were revealed as fine-scale structure detected in the samples from Cameroon, Gambia, Mali, Southwest USA, and Barbados. Our finding helped to explain evolutionary processes in the analyzed West African samples and raise awareness for fine-scale structure resolution when conducting genome-wide association and interaction studies.

Evaluation of Dissolved Organic Carbon Using Synchronized Fluorescence Emission Spectra and Unsupervised Method of Principal Component Analysis (PCA) and Independent Component Analysis (ICA)

Dissolved organic matter (DOM) can be originated from autochthonous or allochthonous sources, where allochthonous DOM can be from pedogenic sources (humic substances—HSs) or anthropogenicsources (wastewater). The analysis of fluorescence emission, excitation, synchronous or excitation-emission matrix (EEM) have been used to identify the main source or probable contribution of dissolved compounds, such as humic acids (HA), fulvic acids (FA) and dissolved organic carbon (DOC) from sewage, but does not quantify. Fluorescence emission is a powerful technique to detect and qualify organic dissolved compounds but fails in quantitative aspects. In this work, we propose an in situ method for direct determination of DOC using synchronous fluorescence spectra with independent component analysis (ICA). Well known standard solutions were used for method development and validation. In this work, we show that it is possible to predict the number of independent contributions using an unsupervised method based on iterative Principal Component Analysis and Independent Component Analysis (PCA-ICA) approach over combined matrix results. Within these results it’s also possible to see that with a very small amount of independent components it is possible to describe environmental samples of HA, FA and primary productivity (PP).