Combination Of Principal Component Analysis Research Articles

A combined principal component analysis and clustering approach for exploring enormous renovation design spaces

This paper presents a new approach for supporting renovation designers in exploring the vast landscape of renovation scenarios. The approach integrates renovation scenario sampling (based on a semantic domain model of renovation, called NovaDM), scenario clustering (specifically k-means) in terms of Key Performance Indicator evaluations, and dimensionality reduction (specifically Principal Component Analysis) so that the sampled scenarios and discovered clusters can be visualized in a meaningful way. The approach is empirically evaluated on a large residential building case study in Denmark. Case study results show that the visualization captures and communicates critical high-level information about the "structure" of the enormous, complex underlying renovation design space, in terms of qualitative KPI profiles (e.g. fairly low privacy, very high cost, etc.), thus significantly enhancing renovation decision making.

Computer assisted mitotic figure detection in histopathology images based on DenseNetPCA framework

Mitotic detection in histopathology images is considered a golden standard method adopted for the purpose to study tumour growth. The procedure results in clinical information about critical factors such as disease prognosis, details about the aggressiveness of the cancer stage. Due to structural similarities that exist between mitosis and non-mitosis, it is a challenging task for pathologists to classify these cells in whole slide images (WSI) manually and it is labor-intensive work. In this paper, to accurately detect mitosis, we constructed a supervised deep framework called DenseNet combined Principal Component Analysis (PCA) model for mitosis classification. This ensemble framework is accurately segmenting the cells and classifies the cells into mitotic or nonmitotic figures. The framework consists of three stages (1) Instance level deep features extraction by DenseNet 121 architecture (2) PCA based feature subset selection (3) mitosis classification. Experimental results were conducted on the available benchmark dataset, MITOS-ATYPIA 14 Hammamatsu Scanner images. The results obtained by our mitosis detection framework shows the effectiveness of the approach.

Measuring Technical Efficiency of Hospitals affiliated with Shahrekord University of Medical Sciences, Using a Combination Method Data Envelopment Analysis (DEA) – Principle Component Analysis (PCA)

Background: Measuring the efficiency of hospitals due to the high proportion of budget allocated to them on the one hand, and the need to ensure the best practices regarding the use of scarce resources on the other hand, is of particular importance. The purpose of this study is to evaluate the technical efficiency of the affiliated hospitals of Shahrekord University of Medical Sciences by using a combination of Principal Component Analysis and (PCA) & Data Envelopment Analysis (DEA). Methods: This was an analytical and cross-sectional study measuring the technical efficiency of all 8 hospitals affiliated to Shahrekord University of Medical Sciences. The required information was collected from the medical records unit of each hospital. For better differentiation between efficient and inefficient units, and the increase of research accuracy and further differentiation between hospitals in terms of efficiency, at first, 17 indicators were selected to assess and adjust these parameters to 3 components proportional to the number of the hospitals by using PCA and SPSS 16 software. After doing the PCA, 7 studied input variables became 7 principal components among which the first input component reflecting the 83 % of scattering data was selected as principal input component, and for being more influenced by human resource variables, it was named as a human resource index. Furthermore, among the output variables, the first 2 output components, which represented 76% of the variance of the data, were selected as the 2 principal components of the output for the study, which were mostly affected by these variables, respectively, the number of admissions and length of stay. Then, the modified input and output components were entered into the software Windeap 2.1 and the technical efficiency of hospitals and their rank were calculated by assuming constant and variable efficiency with respect to the scale. In order to evaluate the effect of using the combined method instead of the conventional method of efficiency measurement, the results of the PCA - DEA method were compared with the results of the conventional DEA method. Results: The result of DEA on the selected components showed the capacity to upgrade the Technical Efficiency (TE) of hospitals is 15 % (TE: 0.852). Moreover, out of 8 hospitals, 1 hospital was increasing return to scale, 3 decreasing returns to scale and 4 constant returns to scale. The technical efficiency of 3 hospitals was 1 (TE = 1), 2 hospitals had the technical efficiency between 0.80 to 1 (1 > TE > 0.80) and that in 3 hospitals was less than 0.80 (TE < 0.80). The scale efficiency for 50 % of hospitals and the management efficiency for 62/5 % of them were equal 1. Conclusion: The average of total technical efficiency, management efficiency and scale efficiency were calculated to be 0.999, 1 and 0.999, respectively based on the usual comprehensive analysis method; while using the combined method, the average total technical efficiency, management efficiency and scale efficiency were 0.852, 0.947 and 0.902 respectively. The results confirm that the use of PCA method, due to its important role in reducing alignments, increases research accuracy and better differentiates between hospitals in terms of efficiency.

An ecosystem model based composite indicator, representing sustainability aspects for comparison of forest management strategies

Forests provide a wide range of ecosystem services, and ecosystem models can be applied to assess the contribution of different forest management strategies to climate mitigation and adaptation. Complex model output and trade-offs between environmental, economic, and social sustainability dimensions are difficult to convey. To facilitate stakeholder communication, we developed composite indicators based on ten ecosystem service indicators obtained from ecosystem model simulations representing 19 forestry management strategies across three ecoregions and climate scenario projections in Sweden. Eight alternative composites were generated around a central framework addressing sustainability aspects in terms of wood production, preservation of biodiversity, climate change mitigation and adaptation (risk management). A combination of principal component analysis, exploratory factor analysis, Cronbach’s coefficient alpha, and hierarchical cluster analysis was applied to account for the statistical relationships between indicators. Z-score normalization was superior to min-max normalization in capturing differences among management strategies. Two weighting schemes were applied, based on policy prioritizations between sub-components that reflected 1) current policy with an equal emphasis on production and biodiversity, and 2) a stronger focus on nature protection. Equal emphasis generated a larger range of scores (76.0 ± 21.2) than the focus on nature protection (32.0 ± 5.8), as the latter would provide less production benefits and thereby fewer trade-offs between production and other aspects. The final scores of the 19 management strategies fell within a variance boundary of each other, showing their contribution to different policy targets and the usefulness of combining strategies at the landscape level. The composites displayed agreement across regions and scenarios. They indicated that a shift from even-aged conifer monocultures towards a combination of continuous cover, broadleaf-mixture, and unmanaged would work well for balancing goals under changing climate conditions.

Genomic signatures of domestication and adaptation during geographical expansions of rice cultivation

Rice (Oryza sativa L.), a global staple food now grown on all inhabited continents, was domesticated from its wild progenitor, O. rufipogon Griff., in tropical and subtropical regions of Asia (Oka, 1988). After domestication, the expansions of rice landraces into the present-day range required a diverse array of adaptations to local environments, which included changes in daylight sensitivity, expanded thermal tolerance (for excess cold and heat), adaptations to water availability (drought and waterlogging), and resistance to biotic stresses (Garris et al., 2005; Glaszmann, 1987).

Dry mechanochemical synthesis of ethenzamide and saccharin 1:1 cocrystal and their evaluation using powder X-ray diffraction and FT-MIR and NIR spectroscopy

The purpose of this study was to evaluate effects of dry mechanochemical synthesizes on ethenzamide (EZ) and saccharin (SA) mixture. EZ and SA mixture were evaluated after grinding by mid and near infrared spectroscopy, powder X-ray diffraction (PXRD) and thermal analysis. During the grind, EZ and SA mixture was transformed into their 1:1 cocrystal form. Measurement time course data was applied to multi variate analysis of principal component analysis (PCA). Their cocrystallization could decompose two steps reactions. At first, EZ crystal transform into amorphous phase and their amorphous react with SA molecules after them, hydrogen bond networks were constructed and produce cocrystal was suggested. Combination PCA and several measurement data enable to extract useful chemical information.

First-in-human pilot study of snapshot multispectral endoscopy for early detection of Barrett's-related neoplasia.

.Significance: The early detection of dysplasia in patients with Barrett’s esophagus could improve outcomes by enabling curative intervention; however, dysplasia is often inconspicuous using conventional white-light endoscopy.Aim: We sought to determine whether multispectral imaging (MSI) could be applied in endoscopy to improve detection of dysplasia in the upper gastrointestinal (GI) tract.Approach: We used a commercial fiberscope to relay imaging data from within the upper GI tract to a snapshot MSI camera capable of collecting data from nine spectral bands. The system was deployed in a pilot clinical study of 20 patients (ClinicalTrials.gov NCT03388047) to capture 727 in vivo image cubes matched with gold-standard diagnosis from histopathology. We compared the performance of seven learning-based methods for data classification, including linear discriminant analysis, -nearest neighbor classification, and a neural network.Results: Validation of our approach using a Macbeth color chart achieved an image-based classification accuracy of 96.5%. Although our patient cohort showed significant intra- and interpatient variance, we were able to resolve disease-specific contributions to the recorded MSI data. In classification, a combined principal component analysis and -nearest-neighbor approach performed best, achieving accuracies of 95.8%, 90.7%, and 76.1%, respectively, for squamous, non-dysplastic Barrett’s esophagus and neoplasia based on majority decisions per-image.Conclusions: MSI shows promise for disease classification in Barrett’s esophagus and merits further investigation as a tool in high-definition “chip-on-tip” endoscopes.

Predictive Modeling and Correlated Response Optimization of Polymethylmethacrylate (PMMA)-Based Bio-Nano-Composite Material Using a Hybrid Module

Polymethylmethacrylate (PMMA) is commonly known as bone cement, having good biocompatibility, mechanical qualities. It is extensively used in the biomedical sector as a synthetic bone material, orthopedic surgery and dental applications. However, some primary machining is required to achieve the tailored shape, size and finish before application in the human body. This study focuses on the machining (drilling) behavior of the developed PMMA-based Hydroxyapatite (PMMA-HA) bio-nano- composites. The machining efficiency and parametric control were estimated using a combined principal component analysis (PCA) module and evaluation based on distance from average solution (EDAS). The Hydroxyapatite (HA) weight percentage (wt.%), spindle speed (SPEED) and tool material (TOOL) viz. HSS, Carbide and TiAlN are chosen according to the Taguchi-based experimental array. The objective is to get the best possible machining responses, such as the material removal rate (MRR), mean surface roughness (Ra) and circularity error ([Formula: see text] using the PCA-EDAS hybrid module. The optimal condition is found as the HSS drilling bit, 10%[Formula: see text]wt.%, SPEED-1428[Formula: see text]rpm with an improvement of 30.53%, 21.15% and 41.9% in MRR, Ra and [Formula: see text]-ERROR, respectively. The microstructural investigation scanning electron microscope (SEM) shows the excellent morphology and quality of the drilled hole in the proposed composites. Also, an X-ray diffraction (XRD) analysis of the prepared sample was done to ensure the proper reinforcement. The flexural test shows a significant expansion in the mechanical property due to the presence of HA in PMMA

Potentially Toxic Elements (PTEs) Composition and Human Health Risk Assessment of PM10 on the Roadways of Industrial Complexes in South Korea

Road and industrial origin particulate matters (PM) are a significant source of potentially toxic elements (PTEs), with health risks to the surrounding residents. In Korea for 60 years, although industries, roads and automobiles have increased aggressively, there are still few PTEs data in PM in road-deposited sediment (RDS) of industrial complexes (ICs). Therefore, this study aimed to investigate the PTE composition of on-road PM10 from nine major ICs and its pollution degree in Korea and evaluate its human health risks. The geo-accumulation index (Igeo) and pollution load index (PLI) elucidated that on-road PM10 were severely polluted by Sb, Zn, Ag and Pb. A combination of principal component analysis (PCA) and chemical tracers was used to define the PTEs sources. The results showed that non-exhaust emission from vehicles’ activity is the primary source of PTEs in on-road PM10, and industrial emissions are the secondary source. The riskiest pathway on carcinogenic and non-carcinogenic by on-road PM10 with PTEs was in-gestion. Traffic origin PTEs including Pb, As, Sb and Cd had a more significant impact on carcinogenic and non-carcinogenic health than those of industrial origins. These results could help mitigate public health risks arising from on-road PM10 and improve air quality in ICs.

Q-Rung orthopair fuzzy TOPSIS method and the application to information service quality evaluation in online health community

Since the information quality in the online health community is very important for users to obtain valuable health information, information quality evaluation is a necessary research that involves a multi-attribute decision-making (MADM) problem. However, few researches have been done to address both the construction of evaluation criteria and the expression and processing of fuzzy information, especially in online health community. This paper proposes a novel evaluation framework of information service quality combined principal component analysis (PCA) method with the TOPSIS method under q-rung orthopair fuzzy set (q-ROFS) environment. An accurate evaluation criteria system is optimized by the PCA method, and the q-ROF TOPSIS method is proposed to process larger space of fuzzy evaluation information and overcome information loss and information distortion, in which a new distance measure between q-ROFSs is defined and an entropy weight model is initiated to determine the unknown weight of attribute. Moreover, a numerical example is performed to prove the practicability and superiority of the method through comparative analysis, which gives clear results of information quality evaluation of four online health communities. This research ends with fuzzy decision-making theory and application, and provides references for standardizing and improving the information quality of online health communities.

Hydrological Similarity-Based Parameter Regionalization under Different Climate and Underlying Surfaces in Ungauged Basins

Hydrological similarity-based parameter regionalization is the dominant method used for runoff prediction in ungauged basin. However, the application of this approach depends on assessing hydrological similarity between basins. This study used data for runoff, climate, and the underlying surface of the Hulan River Basin and Poyang Lake Basin to construct a novel physical hydrological similarity index (HSI). The index was used to compare the efficiency of transfer of the parameters of commonly used regionalization methods and to finally apply parameters to ungauged basins. The results showed that: (1) Precipitation is the main climatic factor regulating magnitude of runoff in the Poyang Lake Basin. Spring runoff in Hulan River Basin was regulated by precipitation and temperature. (2) The GR4J and CemaNeigeGR4J models achieved reasonable simulations of runoff of Poyang Lake Basin and Hulan River Basin. Although CemaNeigeGR4J considers snowmelt, the model simulations of spring runoff in the Hulan River Basin were not accurate. (3) There was a significant correlation between climate, the underlying surface, and hydrological model parameters. There were fewer significant correlations between environmental factors and between environmental factors and hydrological model parameters in the Hulan River Basin compared to those in the Poyang Lake Basin, possibly due to less sub-basins in the Hulan River Basin. (4) The HSI based on a combination of principal component analysis and the entropy method efficiently identified the most similar gauged basin for an ungauged basin. A significant positive correlation existed between the HSI and parameter transfer efficiency. The relationship between the HSI and transfer efficiency could be represented by logistic regression and linear regression in the Poyang Lake Basin and Hulan River Basin, respectively. The HSI was better able to quantify the hydrological similarity between basins in terms of climate and underlying surface and can provide a scientific reference for the transfer of hydrological model parameters in an ungauged basin.

Detecting total acid content quickly and accurately by combining hyperspectral imaging and an optimized algorithm method

AbstractThe total acid content of Daqu during fermentation directly influences the quality of the Daqu. Hyperspectral imaging (HSI) was used to rapidly and accurately detect the total acid content of Daqu. The performances of two spectral ranges obtained by two hyperspectral cameras for Daqu total acid content were compared. Different pretreatment methods were used to build the partial least squares regression (PLSR) model, and the optimal pretreatment for the corresponding spectra was determined. A combination of principal component analysis (PCA) and successive projection algorithm (SPA) was used to select the feature wavelengths. The PCA algorithm optimized by SPA was more accurate than the PCA algorithm alone when extracting the feature wavelengths. PLSR, backpropagation neural network (BP), and the backpropagation‐genetic algorithm (BP‐GA) model were developed to predict the total acid content of the Daqu samples by using the full wavelengths and feature wavelengths, respectively. The predictive performance of each model was compared and analyzed. The optimal model was the MSC + PCA‐SPA+BP‐GA model ( = 0.8832, RMSEC = 0.1163, = 0.9724, RMSEP = 0.0593, RPD = 4.2860, AB_RMSE = 0.0570). In addition, the distribution of total acid content within the region of interest (ROI) of Daqu samples was visualized at different fermentation stages using the optimized model. These results show that HSI technology can rapidly and precisely detect the total acid content of Daqu and visualize the content distribution, which provides important technical support for upgrading and transforming the liquor.Practical ApplicationsDaqu is the saccharifying agent, leavening agent, and aroma‐generating agent of liquor brewing. During the fermentation process, the total acid content is an important indicator for the quality evaluation of Daqu. Therefore, it is highly significant to detect the total acid content in Daqu quickly and accurately. The HSI technique is widely used to detect the contents of various substances in food. In this study, the HSI technique combining the PCA‐SPA and BP‐GA algorithms was established to detect the total acid content of Daqu, and a satisfactory result was obtained, showing that HSI technology combined with an optimized algorithm could be used to realize rapid and accurate detection of the total acid content of Daqu. In addition, the predictive model proposed in this study is not only applicable for detecting other food substances but also this method has great potential for real‐time indicators detection in winery for future work.

High Wind Speed Inversion Model of CYGNSS Sea Surface Data Based on Machine Learning

In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at sea surface. The L1-level satellite-based data from the Cyclone Global Navigation Satellite System (CYGNSS), together with the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) data, constitute the original sample set, which is processed and trained with Support Vector Regression (SVR), the combination of Principal Component Analysis (PCA) and SVR (PCA-SVR), and Convolutional Neural Network (CNN) methods, respectively, to finally construct a sea surface high wind speed inversion model. The three models for high wind speed inversion are certified by the test data collected during Typhoon Bavi in 2020. The results show that all three machine learning models can be used for high wind speed inversion on sea surface, among which the CNN method has the highest inversion accuracy with a mean absolute error of 2.71 m/s and a root mean square error of 3.80 m/s. The experimental results largely meet the operational requirements for high wind speed inversion accuracy.

How Perceived Adequacy of Open Public Space Is Related to Objective Green Space and Individuals’ Opinions of Area-Level Quality

There is substantial evidence of links between green spaces and better health status. However, the ways in which green space are measured are varied, including both objective and subjective measures. This paper examines factors associated with individuals’ perceiving a lack of open public space, including the objective quantity of green space and wider problems in the area. This is analysed using a combination of principal component analysis and logistic regression. We use data from the Healthy Ireland Survey 2016, a cross sectional survey of over 7000 adults in Ireland. The results suggest that objective green space has a significant negative association with whether an individual perceives a problem with lack of open public space. The analysis also reveals that area safety, area cleanliness, and area service provision are important factors explaining perceived adequacy of open public space. The findings of this analysis have implications for the methods by which researchers and policymakers measure and analyse green space in an area.

House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model

House price prediction: A data-centric aspect approach on performance of combined principal component analysis with deep neural network model

A NEW CRITERION FOR DRILLING MACHINABILITY EVALUATION OF NANOCOMPOSITES MODIFIED BY GRAPHENE/CARBON FIBER EPOXY MATRIX AND OPTIMIZATION USING COMBINED COMPROMISE SOLUTION

This article describes new control criteria and robust optimization methodology to balance drilling parameters and machining characteristics. Experimentation was performed according to response surface methodology (RSM) using a TiAlN coated SiC tool. The full drilling force signal and cutting parameters tested are categorized into five stages, indicating the drilling tool-workpiece interactions’ different statuses. Principal component analysis (PCA) assigns real response priority weight during the aggregation of conflicting characteristics. The hybrid module of combined compromise solution and PCA (CoCoSo–PCA) is used to decide the optimal parametric setting. It efficiently undertakes a trade-off between minimal thrust ([Formula: see text][Formula: see text]N), torque ([Formula: see text][Formula: see text]Nm) surface roughness ([Formula: see text]m). A regression model between input parameters and output function was established using RSM quadratic model. The validation experiment shows significant improvement, and the proposed module can be recommended for quality-productivity characteristics control.

Share price prediction of aerospace relevant companies with recurrent neural networks based on PCA

The capital market plays a vital role in marketing operations for the rapid development of the aerospace industry. However, due to the uncertainty and complexity of the stock market and many cyclical factors, the stock prices of listed aerospace companies fluctuate significantly. This makes the share price prediction challengeable. To improve the prediction of share price for aerospace industry sector and well understand the impact of various indicators on stock prices, we provided a hybrid prediction model by the combination of Principal Component Analysis (PCA) and Recurrent Neural Networks (RNN).We investigated two types of aerospace industries (manufacturer and operator). The experimental results show that PCA could improve both accuracy and efficiency of prediction. Various factors could influence the performance of prediction models, such as finance data, extracted features, optimisation algorithms, and parameters of the prediction model. The selection of features may depend on the stability of historical data: technical features could be the first option when the share price is stable, whereas fundamental features could be better when the share price has high fluctuation. The delays of RNN also depend on the stability of historical data for different types of companies. It would be more accurate through using short-term historical data for aerospace manufacturers, whereas using long-term historical data for aerospace operating airlines.The developed model could be an intelligent agent in an automatic stock prediction system, with which, the financial industry could make a prompt decision for their economic strategies and business activities in terms of predicted future share price, thus improving the return on investment. The study is for the prediction of aerospace industries at pre-COVID-19 time. Currently, COVID-19 severely influences aerospace industries. The developed approach can be used to predict the share price of aerospace industries at post COVID-19 time.

Accelerated mapping of electronic density of states patterns of metallic nanoparticles via machine-learning

Within first-principles density functional theory (DFT) frameworks, it is challenging to predict the electronic structures of nanoparticles (NPs) accurately but fast. Herein, a machine-learning architecture is proposed to rapidly but reasonably predict electronic density of states (DOS) patterns of metallic NPs via a combination of principal component analysis (PCA) and the crystal graph convolutional neural network (CGCNN). With the PCA, a mathematically high-dimensional DOS image can be converted to a low-dimensional vector. The CGCNN plays a key role in reflecting the effects of local atomic structures on the DOS patterns of NPs with only a few of material features that are easily extracted from a periodic table. The PCA-CGCNN model is applicable for all pure and bimetallic NPs, in which a handful DOS training sets that are easily obtained with the typical DFT method are considered. The PCA-CGCNN model predicts the R2 value to be 0.85 or higher for Au pure NPs and 0.77 or higher for Au@Pt core@shell bimetallic NPs, respectively, in which the values are for the test sets. Although the PCA-CGCNN method showed a small loss of accuracy when compared with DFT calculations, the prediction time takes just ~ 160 s irrespective of the NP size in contrast to DFT method, for example, 13,000 times faster than the DFT method for Pt147. Our approach not only can be immediately applied to predict electronic structures of actual nanometer scaled NPs to be experimentally synthesized, but also be used to explore correlations between atomic structures and other spectrum image data of the materials (e.g., X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy).

Author identification in bibliographic data using deep neural networks

Author name disambiguation (AND) is a challenging task for scholars who mine bibliographic information for scientific knowledge. A constructive approach for resolving name ambiguity is to use computer algorithms to identify author names. Some algorithm-based disambiguation methods have been developed by computer and data scientists. Among them, supervised machine learning has been stated to produce decent to very accurate disambiguation results. This paper presents a combination of principal component analysis (PCA) as a feature reduction and deep neural networks (DNNs), as a supervised algorithm for classifying AND problems. The raw data is grouped into four classes, i.e., synonyms, homonyms, homonyms-synonyms, and non-homonyms-synonyms classification. We have taken into account several hyperparameters tuning, such as learning rate, batch size, number of the neuron and hidden units, and analyzed their impact on the accuracy of results. To the best of our knowledge, there are no previous studies with such a scheme. The proposed DNNs are validated with other ML techniques such as Naïve Bayes, random forest (RF), and support vector machine (SVM) to produce a good classifier. By exploring the result in all data, our proposed DNNs classifier has an outperformed other ML technique, with accuracy, precision, recall, and F1-score, which is 99.98%, 97.98%, 97.86%, and 99.99%, respectively. In the future, this approach can be easily extended to any dataset and any bibliographic records provider.

On the Achievability of Blind Source Separation for High-Dimensional Nonlinear Source Mixtures.

For many years, a combination of principal component analysis (PCA) and independent component analysis (ICA) has been used for blind source separation (BSS). However, it remains unclear why these linear methods work well with real-world data that involve nonlinear source mixtures. This work theoretically validates that a cascade of linear PCA and ICA can solve a nonlinear BSS problem accurately-when the sensory inputs are generated from hidden sources via nonlinear mappings with sufficient dimensionality. Our proposed theorem, termed the asymptotic linearization theorem, theoretically guarantees that applying linear PCA to the inputs can reliably extract a subspace spanned by the linear projections from every hidden source as the major components-and thus projecting the inputs onto their major eigenspace can effectively recover a linear transformation of the hidden sources. Then subsequent application of linear ICA can separate all the true independent hidden sources accurately. Zero-element-wise-error nonlinear BSS is asymptotically attained when the source dimensionality is large and the input dimensionality is sufficiently larger than the source dimensionality. Our proposed theorem is validated analytically and numerically. Moreover, the same computation can be performed by using Hebbian-like plasticity rules, implying the biological plausibility of this nonlinear BSS strategy. Our results highlight the utility of linear PCA and ICA for accurately and reliably recovering nonlinearly mixed sources and suggest the importance of employing sensors with sufficient dimensionality to identify true hidden sources of real-world data.