Dimension Selection for Feature Selection and Dimension Reduction with Principal and Independent Component Analysis

This letter is concerned with the problem of selecting the best or most informative dimension for dimension reduction and feature extraction in high-dimensional data. The dimension of the data is reduced by principal component analysis; subsequent application of independent component analysis to the principal component scores determines the most nongaussian directions in the lower-dimensional space. A criterion for choosing the optimal dimension based on bias-adjusted skewness and kurtosis is proposed. This new dimension selector is applied to real data sets and compared to existing methods. Simulation studies for a range of densities show that the proposed method performs well and is more appropriate for nongaussian data than existing methods.

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Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-357 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 357-1-357-6

Author(s):

Luisa F. Polanía ◽

Raja Bala ◽

Ankur Purwar ◽

Paul Matts ◽

Martin Maltz

Keyword(s):

Independent Component Analysis ◽

Spectral Reflectance ◽

Source Separation ◽

Principal Component ◽

Component Analysis ◽

Previous Method ◽

Independent Component ◽

Light Transport ◽

Camera Model ◽

Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

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A comparative study of principal component analysis and independent component analysis in eddy current pulsed thermography data processing

Review of Scientific Instruments ◽

10.1063/1.4823521 ◽

2013 ◽

Vol 84 (10) ◽

pp. 104901 ◽

Cited By ~ 15

Author(s):

Libing Bai ◽

Bin Gao ◽

Shulin Tian ◽

Yuhua Cheng ◽

Yifan Chen ◽

...

Keyword(s):

Principal Component Analysis ◽

Independent Component Analysis ◽

Comparative Study ◽

Data Processing ◽

Eddy Current ◽

Principal Component ◽

Component Analysis ◽

Independent Component ◽

Pulsed Thermography

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Improving gene function predictions using independent transcriptional components

Nature Communications ◽

10.1038/s41467-021-21671-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Carlos G. Urzúa-Traslaviña ◽

Vincent C. Leeuwenburgh ◽

Arkajyoti Bhattacharya ◽

Stefan Loipfinger ◽

Marcel A. T. M. van Vugt ◽

...

Keyword(s):

Independent Component Analysis ◽

High Throughput Sequencing ◽

Principal Component ◽

Component Analysis ◽

Independent Component ◽

Sequencing Data ◽

New Members ◽

High Throughput Sequencing Data ◽

Gene Sets ◽

Functional Understanding

AbstractThe interpretation of high throughput sequencing data is limited by our incomplete functional understanding of coding and non-coding transcripts. Reliably predicting the function of such transcripts can overcome this limitation. Here we report the use of a consensus independent component analysis and guilt-by-association approach to predict over 23,000 functional groups comprised of over 55,000 coding and non-coding transcripts using publicly available transcriptomic profiles. We show that, compared to using Principal Component Analysis, Independent Component Analysis-derived transcriptional components enable more confident functionality predictions, improve predictions when new members are added to the gene sets, and are less affected by gene multi-functionality. Predictions generated using human or mouse transcriptomic data are made available for exploration in a publicly available web portal.

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