The study of face recognition based on hybrid principal components analysis and independent component analysis

In this work we apply Independent Component Analysis (ICA) to the identification of the factors driving spot rates curve movements. A comparison between the standard Principal Components Analysis (PCA) approach and ICA is carried out both from a theoretical point of view, critically analyzing the negentropy based approach to ICA, and from an empirical point of view, where the performance of immunization strategies based on PCA and ICA are tested.

Download Full-text

Face recognition based on modified discriminant independent component analysis

2016 6th International Conference on Computer and Knowledge Engineering (ICCKE) ◽

10.1109/iccke.2016.7802116 ◽

2016 ◽

Cited By ~ 2

Author(s):

Maryam Mollaee ◽

Mohammad Hossein Moattar

Keyword(s):

Face Recognition ◽

Independent Component Analysis ◽

Component Analysis ◽

Independent Component

Download Full-text

Face Recognition Algorithm Based on Kernel Collaborative Representation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.3590 ◽

2013 ◽

Vol 756-759 ◽

pp. 3590-3595

Author(s):

Liang Zhang ◽

Ji Wen Dong

Keyword(s):

Face Recognition ◽

Principal Components Analysis ◽

Principal Components ◽

Recognition Algorithm ◽

Experimental Results ◽

Collaborative Representation ◽

Nonlinear Structures ◽

Components Analysis ◽

Kernel Principal Components Analysis ◽

The Relationship

Aiming at solving the problems of occlusion and illumination in face recognition, a new method of face recognition based on Kernel Principal Components Analysis (KPCA) and Collaborative Representation Classifier (CRC) is developed. The KPCA can obtain effective discriminative information and reduce the feature dimensions by extracting faces nonlinear structures features, the decisive factor. Considering the collaboration among the samples, the CRC which synthetically consider the relationship among samples is used. Experimental results demonstrate that the algorithm obtains good recognition rates and also improves the efficiency. The KCRC algorithm can effectively solve the problem of illumination and occlusion in face recognition.

Download Full-text

Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

10.5772/intechopen.101860 ◽

2022 ◽

Author(s):

Jaime González Maiz Jiménez ◽

Adán Reyes Santiago

Keyword(s):

Principal Component Analysis ◽

Stock Market ◽

Principal Components Analysis ◽

Principal Components ◽

Systematic Risk ◽

Principal Component ◽

Component Analysis ◽

Market Capitalization ◽

The Us ◽

Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

Download Full-text

Tensor Independent Component Analysis and Tensor Non-Negative Factorization

Advances in Information and Communication Technology Education - Advanced Pattern Recognition Technologies with Applications to Biometrics ◽

10.4018/978-1-60566-200-8.ch010 ◽

2011 ◽

pp. 202-225

Author(s):

David Zhang ◽

Fengxi Song ◽

Yong Xu ◽

Zhizhen Liang

Keyword(s):

Face Recognition ◽

Independent Component Analysis ◽

Component Analysis ◽

Independent Component ◽

Biometric Recognition ◽

Subspace Analysis ◽

Potential Applications

In this chapter, we describe two tensor-based subspace analysis approaches (tensor ICA and tensor NMF) that can be used in many fields like face recognition and other biometric recognition. Section 10.1 gives the background and development of the two tensor-based subspace analysis approaches. Section 10.2 introduces tensor independent component analysis. Section 10.3 presents tensor nonnegative factorization. Section 10.4 discusses some potential applications of these two subspace analysis approaches in biometrics. Finally, we summarize this chapter in Section 10.5.

Download Full-text