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Independent component analysis: an introduction ...
Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set. This rapidly evolving technique is currently finding applications in analysis of biomedical signals (e.g. ERP, EEG, fMRI, optical imaging), and in models of visual receptive fields and separation of speech signals.
 Independent Component Representations for Face Recognition
Keywords: Independent component analysis, ICA, principal component analysis, PCA, face recognition. 1. INTRODUCTION Several advances in face recognition such as "H~lons,~ " "Eigenfa~es,~ " and "Local Feature Analysis4" have employed forms of principal component analysis, which addresses only second-order moments of the input. Principal component...
Independent component analysis: An introduction ...
The goal of this example is to show the properties of source and mixture signals. Given two source signals s 1 = sin (a) and s 2 = r-0.5, where a is in the range of [1,30] with time step 0.05 and r indicates a random number in the range of [0,1]. Fig. 3 shows source signals, histograms, and scatter diagram of both signals. As shown, the two source signals are independent and their histograms ...
Multilinear independent components analysis - IEEE ...
Abstract: Independent components analysis (ICA) maximizes the statistical independence of the representational components of a training image ensemble, but it cannot distinguish between the different factors, or modes, inherent to image formation, including scene structure, illumination, and imaging. We introduce a nonlinear, multifactor model that generalizes ICA.
Independent component analysis, a new concept ... - CiteSeerX
Independent component analysis, a new concept (1994) ... New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. ... network. In addition, the outputs of these filters are as independent as ......
CiteSeerX — Multilinear Independent Components Analysis
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Independent Components Analysis (ICA) maximizes the statistical independence of the representational components of a training image ensemble, but it cannot distinguish between the different factors, or modes, inherent to image formation, including scene structure, illumination, and imaging.
Introduction to Machine Learning 10701
Independent Component Analysis Barnabás Póczos & Aarti Singh . 2 Independent Component Analysis ... edge detection, receptive fields of V1 cells..., deep neural networks ... natural images . 13 STATIC • Image denoising • Microarray data processing • Decomposing the spectra of galaxies • Face recognition • Facial expression ......
Independent Component Analysis (ICA) - Statistics
Independent Component Analysis (ICA) Independent Component Analysis (.pdf) . Independent component analysis (ICA) is a computational method from statistics and signal processing which is a special case of blind source separation. ICA seeks to separate a multivariate signal into additive subcomponents supposing the mutual statistical independence of the non-Gaussian source signals.
Applying Artificial Neural Networks for Face Recognition
4. The Association of Geometric Feature-Based Method and Independent Component Analysis in Facial Feature Extraction One of the most important steps in the face recognition problem is the facial feature extraction. A good feature extraction will increase the performance of face recognition system....
Enhancing Performance of Face Recognition System Using ...
facebook uses face recognition system to help automate user tagging in photographs. To design high performance algorithms for automatic face recognition systems is a challenging task in the field of computer vision and pattern recognition for real time applications. Independent Component Analysis (ICA) is a...
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