Principal component analysis

Wold, Svante; Esbensen, Kim H.; Geladi, Paul

doi:10.1016/0169-7439(87)80084-9

Cited by 9,550 publications

(4,647 citation statements)

References 24 publications

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“…This can take considerable training time, especially when using neural networks [37]. To enable the models to cope with large hyperspectral datasets (e.g., reflectance, transmittance, fluorescence), applying a principal component analysis (PCA) dimensionality reduction step [48] prior to training a model has been implemented. This step consists on projecting the RTM output spectra onto the top eigenvectors computed with PCA, p B, where p is the number of selected components (usually one typically takes enough components to ensure that at least 99.9% of variance is retained), and B is the number of original bands of the spectra.…”

Section: Emulator Theorymentioning

confidence: 99%

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

et al. 2016

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Abstract:Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To overcome this problem, it is proposed to substitute RTMs through surrogate meta-models also named emulators. Emulators approximate the functioning of RTMs through statistical learning regression methods, and can open many new applications because of their computational efficiency and outstanding accuracy. Emulators allow fast global sensitivity analysis (GSA) studies on advanced, computationally expensive RTMs. As a proof-of-concept, three machine learning regression algorithms (MLRAs) were tested to function as emulators for the leaf RTM PROSPECT-4, the canopy RTM PROSAIL, and the computationally expensive atmospheric RTM MODTRAN5. Selected MLRAs were: kernel ridge regression (KRR), neural networks (NN) and Gaussian processes regression (GPR). For each RTM, 500 simulations were generated for training and validation. The majority of MLRAs were excellently validated to function as emulators with relative errors well below 0.2%. The emulators were then put into a GSA scheme and compared against GSA results as generated by original PROSPECT-4 and PROSAIL runs. NN and GPR emulators delivered identical GSA results, while processing speed compared to the original RTMs doubled for PROSPECT-4 and tripled for PROSAIL. Having the emulator-GSA concept successfully tested, for six MODTRAN5 atmospheric transfer functions (outputs), i.e., direct and diffuse at-surface solar irradiance (E di f , E dir ), direct and diffuse upward transmittance (T dir , T di f ), spherical albedo (S) and path radiance (L 0 ), the most accurate MLRA's were subsequently applied as emulator into the GSA scheme. The sensitivity analysis along the 400-2500 nm spectral range took no more than a few minutes on a contemporary computer-in comparison, the same analysis in the original MODTRAN5 would have taken over a month. Key atmospheric drivers were identified, which are on the one hand aerosol optical properties, i.e., aerosol optical thickness (AOT), Angstrom coefficient (AMS) and scattering asymmetry variable (G), mostly driving diffuse atmospheric components, E di f and T di f ; and those affected by atmospheric scattering, L 0 and S. On the other hand, as expected, AOT, AMS and columnar water vapor (CWV) in the absorption regions mostly drive E dir and T dir atmospheric functions. The presented emulation schemes showed very promising results in replacing costly RTMs, and we think they can contribute to the adoption of machine learning techniques in remote sensing and environmental applications.

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Section: Emulator Theorymentioning

confidence: 99%

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

et al. 2016

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“…Different processing methods, such as normalization and scaling, were applied to determine the best discrimination among samples. In the discovery phase, sensor response data were analyzed using principal components analysis to reduce the data from 32 individual responses to vectors or principal components (22). The vectors were calculated to capture the maximum amount of variance in the dataset.…”

Section: Data Processing and Analysis For Discovery And Training Phasementioning

confidence: 99%

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Machado

Laskowski²,

Deffenderfer³

et al. 2005

Am J Respir Crit Care Med

533

429

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Rationale: Electronic noses are successfully used in commercial applications, including detection and analysis of volatile organic compounds in the food industry. Objectives: We hypothesized that the electronic nose could identify and discriminate between lung diseases, especially bronchogenic carcinoma. Methods: In a discovery and training phase, exhaled breath of 14 individuals with bronchogenic carcinoma and 45 healthy control subjects or control subjects without cancer was analyzed. Principal components and canonic discriminant analysis of the sensor data was used to determine whether exhaled gases could discriminate between cancer and noncancer. Discrimination between classes was performed using Mahalanobis distance. Support vector machine analysis was used to create and apply a cancer prediction model prospectively in a separate group of 76 individuals, 14 with and 62 without cancer. Main Results: Principal components and canonic discriminant analysis demonstrated discrimination between samples from patients with lung cancer and those from other groups. In the validation study, the electronic nose had 71.4% sensitivity and 91.9% specificity for detecting lung cancer; positive and negative predictive values were 66.6 and 93.4%, respectively. In this population with a lung cancer prevalence of 18%, positive and negative predictive values were 66.6 and 94.5%, respectively. Conclusion: The exhaled breath of patients with lung cancer has distinct characteristics that can be identified with an electronic nose. The results provide feasibility to the concept of using the electronic nose for managing and detecting lung cancer.

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“…The data set was then randomly sorted into 2 new data sets comprising 2 thirds and 1 thirds of the spectra. These new sets were then used as the calibration and datasets respectively for Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) [28,29], and Partial Least Squares Discriminant Analysis (PLS-DA) [30]. To enable comparison between spectral averages and their standard deviation, mean and standard deviation spectra for each hESC and hiPSC line were calculated using the 2 nd derivative spectra that passed the quality test.…”

Section: Data Preprocessing and Multivariate Data Analysismentioning

confidence: 99%

Fourier transform infrared microspectroscopy reveals unique phenotypes for human embryonic and induced pluripotent stem cell lines and their progeny

Cao

McNaughton

et al. 2013

Journal of Biophotonics

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Journal of BIOPHOTONICSFourier transform infrared (FTIR) microspectroscopy was employed to elucidate the macromolecular phenotype of human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) and their differentiated progeny. Undifferentiated hESCs and hiPSC lines were found to be not clearly distinguishable from each other. However, although both hESC and hiPSC variants appeared to undergo similar changes during differentiation in terms of cell surface antigens, the derived cell types from all cell lines could be discriminated using FTIR spectroscopy. We foresee a possible future role for FTIR microspectroscopy as a powerful and objective investigative and quality control tool in regenerative medicine.Bright field image of human embryonic stem cells in culture.

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Principal component analysis

Cited by 9,550 publications

References 24 publications

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Emulation of Leaf, Canopy and Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis

Detection of Lung Cancer by Sensor Array Analyses of Exhaled Breath

Fourier transform infrared microspectroscopy reveals unique phenotypes for human embryonic and induced pluripotent stem cell lines and their progeny

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