|
Web
Education
 Region-Based Representations for Face Recognition
Region-Based Representations for Face Recognition BENJAMIN J. BALAS and PAWAN SINHA ... Given that this does not occur, we can infer that fine edge information is not a critical prerequisite for face recognition. ... Belhumer et al. 1997] or independent components analysis (ICA) [Bartlett et al. 1998]. These techniques are agnostic about what ......
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.
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.
Principal Manifolds and Probabilistic Subspaces for Visual ...
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and nonlinear Kernel PCA (KPCA) are examined and tested in a visual recognition experiment using 1,800+ facial images from the "FERET"database....
Financial time series forecasting using independent ...
An example is used for illustrating the concept of the TnA method.Fig. 2 shows four financial time series data, each of size 1 × 794, which can be combined as a mixture matrix X of size 4 × 794. After using ICA method to the matrix X, a de-mixing matrix W of size 4 × 4 and four ICs, each of size 1 × 794, can be estimated. The profiles of those four ICs are shown in Fig. 3.
Statistical Learning
Adv. Topics in Statistical Learning: CSE 692 Fall 2007. CLASS INFORMATION: Lectures: Tue 1:50 -- 3:40 pm Location: Computer Science Bldg. room 1441 Instructor: Prof. M. Alex O. Vasilescu Office Hours: Wed 1-2pm or by appointment Prerequisites: Linear Algebra, Probability, or consent of the instructor.. Course Description:
An Introduction to Independent Components Analysis (ICA)
Independent Component Analysis. 2001 • Stone, James. Independent Component Analysis: A Tutorial IdiIntroduction. 2004 • Bishop, Christopher. Pattern Recognition and Machine Learning. 2007 • Shawe‐Taylor, J and N Cristianini. Kernel Methods for Pattern Analysis. 2004...
A Case for the Average-Half-Face in 2D and 3D for Face ...
3. Face Recognition Algorithms We will briefly discuss each of the 6 face recognition methods used in our experiments. 3.1. Eigenfaces Eigenfaces was introduced early [15] on as powerful use of principal components analysis (PCA) to solve problems in face recognition and detection. Eigenfaces is a subspace...
Eeg-based emotion recognition with brain network using ...
EEG-based Emotion Recognition with Brain Network using Independent Components Analysis and Granger Causality CHEN Dongwei , Wu Fang 2, Wang Zhen 2, Li Haifang CHEN Junjie* College of Computer and Software Taiyuan University of Technology Taiyuan China chendwzhbit@sina.cn lihf@tyut.edu.cn chenjj@tyut.edu.cn 2 College of Computer
Independent Principal Component Analysis for biologically ...
Independent Principal Component Analysis (IPCA) makes the assumption that biologically meaningful components can be obtained if most noise has been removed in the associated loading vectors. In IPCA, PCA is used as a pre-processing step to reduce the dimension of the data and to generate the loading vectors.
|
