Principal component analysis research papers

Article

Article "Hyperspectral image analysis for oil spill. This kind of analysis is known as the Disrciminant or Classification Analysis. Whereas, the classifiers applied to the full dataset and region of interest ROI before and after performing principal component analysis PCA. The PCA is utilised to eliminate redundant data, reduce the vast amount of information and consequently, decrease the processing times.

Non-Greedy L21-Norm Maximization for Principal Component Analysis

Non-Greedy L21-Norm Maximization for Principal Component Analysis The links between the columns of the data table need to be determined. The dimensionality reduction algorithms, Principal Component Analysis PCA 1 is one of the most widely used algorithms due to its simplicity and effectiveness.

Establishment and Research on the Model of the Company's.

Establishment and Research on the Model of the Company's. [link] 3D Face Reconstruction from a Single Image Using a Single Reference Face Shape Kemelmacher-Shlizerman, I; Basri, R [link] Cost-Sensitive Face Recognition Zhang, Y; Zhou, Z [link] Age Synthesis and Estimation via Faces: A Survey Fu, Y.; Guo, G.; Huang, T. Li, Jianglong Chang and Zengfu Wang [link] Computer Vision and Image Understanding 3D face reconstructions from photometric stereo using near infrared and visible light Mark F. Zapata [link] Video-based face model fitting using Adaptive Active Appearance Model Xiaoming Liu [link] A new ranking method for principal components analysis and its application to face image analysis Carlos Eduardo Thomaz, Gilson Antonio Giraldi [link] Facial gender classification using shape-from-shading Jing Wu, William A. Establishment and Research on the Model of the Company's Financial Risk Warning Based on Principal Component Analysis and Logistic Regression

An Augmented Lagrangian Approach for Sparse Principal.

An Augmented Lagrangian Approach for Sparse Principal. [link] Face Recognition by Exploring Information Jointly in Space, Scale and Orientation Lei, Z.; Liao, S.; Pietikainen, M.; Li, S. Ghaemmaghami, Ali Aghagolzadeh [link] Color space normalization: Enhancing the discriminating power of color spaces for face recognition Jian Yang, Chengjun Liu, Lei Zhang [link] 3D object classification using salient point patterns with application to craniofacial research Indriyati Atmosukarto, Katarzyna Wilamowska, Carrie Heike, Linda G. An Augmented Lagrangian Approach for Sparse Principal Component Analysis Zhaosong Lu∗ Yong Zhang † July 12, 2009 Abstract Principal component analysis PCA is a widely used technique for data analysis and

Principal Component Analysis Example -

Principal Component Analysis Example - These patterns are shown in the form of two different plots. Be able explain the process required to carry out a Principal Component Analysis/Factor analysis. Be able to carry out a Principal Component Analysis factor/analysis using the psych package in R. Be able to demonstrate that PCA/factor analysis can be undertaken with either raw data or a set of

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