Moving objects classification via category-wise two-dimensional principal component analysis

Alsaqre, Falah; Almathkour, Osama

doi:10.1016/j.aci.2019.02.001

Cited by 5 publications

(2 citation statements)

References 31 publications

(32 reference statements)

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“…In addition, the proposed method was improved by 1.3% over the Y-PD results with the YOLOv2 algorithm. Experiments on the INRIA dataset perform comparative evaluations with four detectors: CAP [23], HOG [22], GAB [24], CWPAC, CW2DPCA [25]and Y-PD [26]. As shown as in Table 1, the results were derived, and higher accuracy were derived than linear-based detection systems.…”

Section: Experiments and Analysismentioning

confidence: 99%

A Boosted 3-D PCA Algorithm Based on Efficient Analysis Method

Lee

Lin

2021

Applied Sciences

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In this paper, we propose a boosted 3-D PCA algorithm based on an efficient analysis method. The proposed method involves three steps that improve image detection. In the first step, the proposed method designs a new analysis method to solve the performance problem caused by data imbalance. In the second step, a parallel cross-validation structure is used to enhance the new analysis method further. We also design a modified AdaBoost algorithm to improve the detector accuracy performance of the new analysis method. In order to verify the performance of this system, we experimented with a benchmark dataset. The results show that the new analysis method is more efficient than other image detection methods.

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Section: Experiments and Analysismentioning

confidence: 99%

A Boosted 3-D PCA Algorithm Based on Efficient Analysis Method

Lee

Lin

2021

Applied Sciences

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“…They have also the advantage of handling and visualizing the results of complex and massive amounts of data [4][5][6][7]. Principal Component Aanalysis (PCA) is a popular method for performing dimension reduction [8] of a set of continuous variables, an effective approach to capture characteristics [9], with the aim of identifying those variables that contribute most to the creation of new, composite variables unlike in feature selection [10], known as principal components or dominant factorial axes. This is achieved via the diagonalization of a symmetric correlation or covariance matrix [11].…”

Section: Introductionmentioning

confidence: 99%

“Automatic” interpretation of multiple correspondence analysis (MCA) results for nonexpert users, using R programming

Moschidis

Markos

Thanopoulos

2022

ACI

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PurposeThe purpose of this paper is to create an automatic interpretation of the results of the method of multiple correspondence analysis (MCA) for categorical variables, so that the nonexpert user can immediately and safely interpret the results, which concern, as the authors know, the categories of variables that strongly interact and determine the trends of the subject under investigation.Design/methodology/approachThis study is a novel theoretical approach to interpreting the results of the MCA method. The classical interpretation of MCA results is based on three indicators: the projection (F) of the category points of the variables in factorial axes, the point contribution to axis creation (CTR) and the correlation (COR) of a point with an axis. The synthetic use of the aforementioned indicators is arduous, particularly for nonexpert users, and frequently results in misinterpretations. The current study has achieved a synthesis of the aforementioned indicators, so that the interpretation of the results is based on a new indicator, as correspondingly on an index, the well-known method principal component analysis (PCA) for continuous variables is based.FindingsTwo (2) concepts were proposed in the new theoretical approach. The interpretative axis corresponding to the classical factorial axis and the interpretative plane corresponding to the factorial plane that as it will be seen offer clear and safe interpretative results in MCA.Research limitations/implicationsIt is obvious that in the development of the proposed automatic interpretation of the MCA results, the authors do not have in the interpretative axes the actual projections of the points as is the case in the original factorial axes, but this is not of interest to the simple user who is only interested in being able to distinguish the categories of variables that determine the interpretation of the most pronounced trends of the phenomenon being examined.Practical implicationsThe results of this research can have positive implications for the dissemination of MCA as a method and its use as an integrated exploratory data analysis approach.Originality/valueInterpreting the MCA results presents difficulties for the nonexpert user and sometimes lead to misinterpretations. The interpretative difficulty persists in the MCA's other interpretative proposals. The proposed method of interpreting the MCA results clearly and accurately allows for the interpretation of its results and thus contributes to the dissemination of the MCA as an integrated method of categorical data analysis and exploration.

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