Independent components analysis as an alternative to principal component analysis and discriminant analysis algorithms in the processing of spectrometric data

2015 ◽  
Vol 70 (9) ◽  
pp. 1055-1061 ◽  
Author(s):  
Yu. B. Monakhova ◽  
A. M. Tsikin ◽  
S. P. Mushtakova
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Components Analysis ◽  
Spectrometric Data ◽  
Independent Components ◽  
Components Analysis

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

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.

scholarly journals Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor)

2020 ◽  
Vol 2 (2) ◽  
pp. 29-38
Author(s):  
Abdur Rohman Harits Martawireja ◽  
Hilman Mujahid Purnama ◽  
Atika Nur Rahmawati
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Kernel Fisher Discriminant Analysis

Pengenalan wajah manusia (face recognition) merupakan salah satu bidang penelitian yang penting dan belakangan ini banyak aplikasi yang menerapkannya, baik di bidang komersil ataupun di bidang penegakan hukum. Pengenalan wajah merupakan sebuah sistem yang berfungsikan untuk mengidentifikasi berdasarkan ciri-ciri dari wajah seseorang berbasis biometrik yang memiliki keakuratan tinggi. Pengenalan wajah dapat diterapkan pada sistem keamanan. Banyak metode yang dapat digunakan dalam aplikasi pengenalan wajah untuk keamanan sistem, namun pada artikel ini akan membahas tentang dua metode yaitu Two Dimensial Principal Component Analysis dan Kernel Fisher Discriminant Analysis dengan metode klasifikasi menggunakan K-Nearest Neigbor. Kedua metode ini diuji menggunakan metode cross validation. Hasil dari penelitian terdahulu terbukti bahwa sistem pengenalan wajah metode Two Dimensial Principal Component Analysis dengan 5-folds cross validation menghasilkan akurasi sebesar 88,73%, sedangkan dengan 2-folds validation akurasi yang dihasilkan sebesar 89,25%. Dan pengujian metode Kernel Fisher Discriminant dengan 2-folds cross validation menghasilkan akurasi rata rata sebesar 83,10%.

2D Image Matrix-Based Discriminator

2006 ◽  
pp. 258-286
Author(s):  
David Zhang ◽  
Xiao-Yuan Jing ◽  
Jian Yang
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Image Projection ◽  
Projection Techniques

This chapter presents two straightforward image projection techniques — two-dimensional (2D) image matrix-based principal component analysis (IMPCA, 2DPCA) and 2D image matrix-based Fisher linear discriminant analysis (IMLDA, 2DLDA). After a brief introduction, we first introduce IMPCA. Then IMLDA technology is given. As a result, we summarize some useful conclusions.

scholarly journals Development of a Piezoelectric-Based Odor Reproduction System

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 870
Author(s):  
Tengteng Wen ◽  
Dehan Luo ◽  
Yongjie Ji ◽  
Pingzhong Zhong
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
Piezoelectric Transducers ◽  
Linear Discriminant ◽  
Reproduction System ◽  
Machine Olfaction

Odor reproduction, a branch of machine olfaction, is a technology through which a machine represents various odors by blending several odor sources in different proportions and releases them. In this paper, an odor reproduction system is proposed. The system includes an atomization-based odor dispenser using 16 micro-porous piezoelectric transducers. The authors propose the use of an electronic nose combined with a Principal Component Analysis–Linear Discriminant Analysis (PCA–LDA) model to evaluate the effectiveness of the system. The results indicate that the model can be used to evaluate the system.

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