The common vector approach and its relation to principal component analysis

This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images.

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Scaled PCA: A New Approach to Dimension Reduction

Management Science ◽

10.1287/mnsc.2021.4020 ◽

2021 ◽

Author(s):

Dashan Huang ◽

Fuwei Jiang ◽

Kunpeng Li ◽

Guoshi Tong ◽

Guofu Zhou

Keyword(s):

Principal Component Analysis ◽

Supervised Learning ◽

Principal Component ◽

Real Data ◽

Component Analysis ◽

General Factor ◽

Macroeconomic Forecasting ◽

New Approach ◽

Learning Technique ◽

The Common

This paper proposes a novel supervised learning technique for forecasting: scaled principal component analysis (sPCA). The sPCA improves the traditional principal component analysis (PCA) by scaling each predictor with its predictive slope on the target to be forecasted. Unlike the PCA that maximizes the common variation of the predictors, the sPCA assigns more weight to those predictors with stronger forecasting power. In a general factor framework, we show that, under some appropriate conditions on data, the sPCA forecast beats the PCA forecast, and when these conditions break down, extensive simulations indicate that the sPCA still has a large chance to outperform the PCA. A real data example on macroeconomic forecasting shows that the sPCA has better performance in general.

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Princicpal Component Analysis of type II supernova V band light-curves

Proceedings of the International Astronomical Union ◽

10.1017/s1743921314013660 ◽

2014 ◽

Vol 10 (S306) ◽

pp. 330-332

Author(s):

Lluís Galbany

Keyword(s):

Principal Component Analysis ◽

Light Curve ◽

Principal Component ◽

Component Analysis ◽

Light Curves ◽

Physical Parameters ◽

Core Collapse ◽

Type Ii ◽

V Band ◽

The Common

AbstractWe present a Principal Component Analysis (PCA) of the V band light-curves of a sample of more than 100 nearby Core collapse supernovae (CC SNe) from [Anderson et al. (2014)]. We used different reference epochs in order to extract the common properties of these light-curves and searched for correlations to some physical parameters such as the burning of 56Ni, and morphological light-curve parameters such as the length of the plateau, the stretch of the light-curve, and the decrements in brightness after maximum and after the plateau. We also used these similarities to create SNe II light-curve templates that will be used in the future for standardize these objects and determine cosmological distances.

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Evidence-Based Statistical Evaluation of Japanese L2-Learners’ Proficiency using Principal Component Analysis

SHS Web of Conferences ◽

10.1051/shsconf/202110201005 ◽

2021 ◽

Vol 102 ◽

pp. 01005

Author(s):

Masafumi Arai ◽

Hajime Tsubaki ◽

Yoshinori Sagisaka

Keyword(s):

Principal Component Analysis ◽

Prediction Method ◽

Principal Component ◽

Objective Evaluation ◽

Component Analysis ◽

Global Scale ◽

L2 Learners ◽

Scale Scores ◽

The Common ◽

Conventional Methods

This paper aims at an automatic evaluation of second language (L2) learners’ proficiencies and tries to analyze English conversation data having 94 statistics and Global Scale scores of the Common European Framework of Reference (CEFR) given to each participant. The CEFR defines Range, Accuracy, Fluency, Interaction and Coherence as 5 subcategories, which constitute the CEFR Global Scale score. The statistics were classified into the CEFR’s 5 subcategories. We used the Principal Component Analysis (PCA), an unsupervised machine learning method, on each subcategory and obtained the participants’ principal component scores (PC scores) of the 5 subcategories for estimation parameters. We predicted the participants’ CEFR Global scores using the Multiple Regression Analysis (MRA). The proposed prediction method using the PC scores was compared with conventional methods with the 94 statistics. Based on the coeﬃcients of determination (R2), the value of the proposed method (0.82) was nearly equivalent to one of values obtained by the conventional methods. Meanwhile, as for standard deviation, the proposed method showed the smallest value in the comparison. The results indicated usability of the PCA and PC scores calculated from the CEFR subcategory data for objective evaluation of L2 learners’ English proficiencies.

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Actual Fe-Li mica series as a series with ☐VI constant but not with AlIV or AlVI

Mineralogical Magazine ◽

10.1180/002646100549607 ◽

2000 ◽

Vol 64 (4) ◽

pp. 755-775 ◽

Cited By ~ 2

Author(s):

Sun Shihua ◽

Yu Jie

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Main Mechanism ◽

New Concepts ◽

The Common ◽

New Interpretation

AbstractTo date, Fe-Li micas have been defined differently from other micas. The purpose of this paper is to reinterpret the actual Fe-Li mica series with new concepts of ‘essential replacement’ (the evolution direction) and ‘composition track’ (the sequence of mica varieties). Two hundred and fifty-eight analyses from the literature are used for this study in the form of eight data groups. The common compositional and substitutional characters of Fe-Li micas have been reinterpreted in light of principal component analysis and a geometric frame of ideal Fe2+-Al-Li micas in space with (Si, AlIV, AlVI, Fe2+, Li, ☐VI, K)-coordinates.In our new interpretation, the actual Fe-Li micas are essentially neither AlIV- nor AlVI-, but ☐VI-constant. The actual Fe-Li micas are the weakest fluctuant relative to the Annite-Polylithionite-Trilithionite-Siderophyllite (APTE) plane. About 90% of variations of actual Fe-Li micas range from the trioctahedral trend described as a sequence along the segment K2Al2Fe5/22+Li☐1/2Si6Al2O20(OH,F)4+x[AlFe−22+Li3SiAl−2] (−1/3≤ x≤5/8). The substitution AlFe−22+Li3SiAl−2 (i.e. 2AlIV +4Fe2+ → 2Si +AlVI +3Li) is the main mechanism that keeps actual Fe-Li micas trioctahedral. More than 8% of variations arise from the dioctahedral trend involving created AlVI- and ☐VI-increasing replacement. The actual Fe-Li mica series comprises the composition trend from Fe2+-biotite to lepidolite. This series is not on the siderophyllite-polylithionite join, but can be expressed ideally as K2Al1+xFe4−4x2+Li1+3x(Si6+2xAl2−2x) O20(OH,F)4 (−1/3 ≤x≤1).

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Construction Formula of Biological Age Using the Principal Component Analysis

BioMed Research International ◽

10.1155/2016/4697017 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 10

Author(s):

Linpei Jia ◽

Weiguang Zhang ◽

Rufu Jia ◽

Hongliang Zhang ◽

Xiangmei Chen

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Component Analysis ◽

Influential Factors ◽

Biological Age ◽

Whole Body ◽

Test Items ◽

Advantages And Disadvantages ◽

The Common ◽

Selection Of

The biological age (BA) equation is a prediction model that utilizes an algorithm to combine various biological markers of ageing. Different from traditional concepts, the BA equation does not emphasize the importance of a golden index but focuses on using indices of vital organs to represent the senescence of whole body. This model has been used to assess the ageing process in a more precise way and may predict possible diseases better as compared with the chronological age (CA). The principal component analysis (PCA) is applied as one of the common and frequently used methods in the construction of the BA formula. Compared with other methods, PCA has its own study procedures and features. Herein we summarize the up-to-date knowledge about the BA formula construction and discuss the influential factors, so as to give an overview of BA estimate by PCA, including composition of samples, choices of test items, and selection of ageing biomarkers. We also discussed the advantages and disadvantages of PCA with reference to the construction mechanism, accuracy, and practicability of several common methods in the construction of the BA formula.

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