A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

Hasan, Basna Mohammed Salih; Abdulazeez, Adnan Mohsin

doi:10.30880/jscdm.2021.02.01.003

Cited by 135 publications

(83 citation statements)

References 58 publications

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“…We integrated similar topics yielded from the LDA models by PCA (Figure 1, Step 9). PCA is a dimensionality reduction technique to extract core components with as little loss of the original information as possible (Salih Hasan & Abdulazeez, 2021). We applied PCA to ease the data visualization and avoid the curse of dimensionality, which may produce unreliable results (Verleysen & Francois, 2005).…”

Section: Methodsmentioning

confidence: 99%

Latent Dirichlet allocation topic modeling of free‐text responses exploring the negative impact of the early COVID‐19 pandemic on research in nursing

Inoue

Fukahori

Matsubara

et al. 2022

Japan Journal Nursing Sci

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Aim To derive latent topics from free‐text responses on the negative impact of the pandemic on research activities and determine similarities and differences in the resulting themes between academic‐based and clinical‐based researchers. Methods We performed a secondary analysis of free‐text responses from a cross‐sectional online survey conducted by the Japan Academy of Nursing Science of its members in early 2020. The participants were categorized into two groups by workplace (academic‐based and clinical‐based researchers). Latent Dirichlet allocation (LDA) topic modeling was used to extract latent topics statistically and list important keywords/text associated with the topics. After organizing similar topics by principal component analysis (PCA), we finally derived topic‐associated themes by reading the keywords/texts and determining the similarity and differences of the themes between the two groups. Results A total of 201 respondents (163 academic‐based and 38 clinical‐based researchers) provided free‐text responses. LDA identified eight and three latent topics for the academic‐based and clinical‐based researchers, respectively. While PCA re‐grouped the eight topics derived from the former group into four themes, no merging of the topics from the latter group was performed resulting in three themes. The only theme common to the two groups was “barriers to conducting research,” with the remaining themes differing between the groups. Conclusions Using LDA topic modeling with PCA, we identified similarities and differences in the themes described in free‐text responses about the negative impact of the pandemic between academic‐based and clinical‐based researchers. Measures to mitigate the negative impact of pandemics on nursing research may need to be tailored separately.

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Section: Methodsmentioning

confidence: 99%

Latent Dirichlet allocation topic modeling of free‐text responses exploring the negative impact of the early COVID‐19 pandemic on research in nursing

Inoue

Fukahori

Matsubara

et al. 2022

Japan Journal Nursing Sci

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“…We can eliminate the eigenvectors that are relatively less significant using PCA. The mathematical equation of PCA is hereunder [24].…”

Section: Principal Components Analysismentioning

confidence: 99%

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Anjaneyulu¹,

Kubendiran²

2023

Computers, Materials &Amp; Continua

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Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In this paper, we have proposed a novel hybrid algorithm utilizing Principal Component Analysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory (LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCA removes unwanted information from the dataset and selects essential features. Secondly, SAE is used to reduce the dimension of input data using onehot encoding so the model can be trained with better speed. Thirdly, LSTM takes the input from SAE, where the data is sorted in ascending order based on the important features and generates the derived value. Finally, KNN Regressor takes information from LSTM to predict traffic flow. The forecasting performance of the PALKNN model is investigated with Open Road Traffic Statistics dataset, Great Britain, UK. This paper enhanced the traffic flow prediction for every hour of a day with a minimal error value. An extensive experimental analysis was performed on the benchmark dataset. The evaluated results indicate the significant improvement of the proposed PALKNN model over the recent approaches such as KNN, SARIMA, Logistic Regression, RNN, and LSTM in terms of root mean square error (RMSE) of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE) of 2.04%.

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“…For example, the Federal Food and Drug Administration (FDA) has endorsed the use of CCS as a primary efficacy endpoint in prodromal AD clinical trials as long as they combine both cognitive and functional measures ( Hoogendam et al, 2014 ). CCS derived from neuropsychological test batteries consist of statistically related measures within that battery ( Alavi et al, 2020 ; Salih Hasan & Abdulazeez, 2021 ; Schneider & Goldberg, 2020 ). The CCS are then given names that represent specific cognitive processes or domains, such as learning and memory, visuospatial abilities, executive function (EF), language, attention and processing speed (which are sometimes included within EF), and intelligence ( Arango-Lasprilla et al, 2017 ; Baek et al, 2012 ; Harp et al, 2021 ; Harrison, 2019 ; Harvey, 2019 ; Hayat et al, 2021 ; Hubley, 2010 ; Lambert et al, 2018 ; Millan et al, 2012 ; Perry et al, 2017 ; Riordan, 2017 ; Schumacher et al, 2019 ; Smits et al, 2015 ; Vigliecca, 2021 ; Wessels et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Principal component analysis (PCA) is one of the most common data reduction techniques used to identify CCS ( Alavi et al, 2020 ; Salih Hasan & Abdulazeez, 2021 ). The primary goal of PCA is to reduce a large dataset into the fewest possible components in order to reduce the dimensionality of the data while maximizing the possible information and variation in the original dataset ( Alavi et al, 2020 ; Salih Hasan & Abdulazeez, 2021 ). In the present study, we used PCA as a data-driven approach to determine the cognitive component structure of a neuropsychological test battery administered to cognitively-normal (predominantly) older adults in the Southern Illinois University (SIU) Longitudinal Cognitive Aging Study (LCAS) ( Pyo et al, 2006 ).…”

Section: Introductionmentioning

confidence: 99%

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Cognitive Component Structure of a Neuropsychological Battery Administered to Cognitively-Normal Adults in the SIU Longitudinal Cognitive Aging Study

Hollinshead

Botchway

Schmidt

et al. 2022

Gerontology and Geriatric Medicine

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We used principal component analysis (PCA) to examine the component structure of a neuropsychological test battery administered to 943 cognitively-normal adults enrolled in the Southern Illinois University (SIU) Longitudinal Cognitive Aging Study (LCAS). Four components explaining the most variance (63.9%) in the dataset were identified: speed/cognitive flexibility, visuospatial skills, word-list learning/memory, and story memory. Regression analyses confirmed that increased age was associated with decreased component scores after controlling for gender and education. Our identified components differ slightly from previous studies using PCA on similar test batteries. Factors such as the demographic characteristics of the study sample, the inclusion of mixed patient and control samples, the inclusion of different test measures in previous studies, and the fact that many neuropsychological test measures assess multiple cognitive processes simultaneously, may help to explain these inconsistencies.

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A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

Cited by 135 publications

References 58 publications

Latent Dirichlet allocation topic modeling of free‐text responses exploring the negative impact of the early COVID‐19 pandemic on research in nursing

Latent Dirichlet allocation topic modeling of free‐text responses exploring the negative impact of the early COVID‐19 pandemic on research in nursing

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Cognitive Component Structure of a Neuropsychological Battery Administered to Cognitively-Normal Adults in the SIU Longitudinal Cognitive Aging Study

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