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Sparse principal component analysis

F.G.W. Dannenberg

Open publisher page 33 citations

Abstract

Principal component analysis (PCA) is a widespread exploratory data analysis tool. Sparse principal component analysis (SPCA) is a method that improves upon PCA by increasing the number of zeros in the loading vectors of PCA results. This makes the results more understandable and more usable. This bachelor's thesis introduces both methods, and includes examples using both real-world data and artifcial data. Also, the behavior of PCA under departure from weakly stationary data is explored.

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What this paper is about

Principal component analysis (PCA) is a widespread exploratory data analysis tool. Sparse principal component analysis (SPCA) is a method that improves upon PCA by increasing the number of zeros in the loading vectors of PCA results. This makes the results more understandable and more usable. This bachelor's thesis introduces both methods, and includes examples using both real-world data and artifcial data. Also, the behavior of PCA under departure from weakly stationary data is explored.

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OpenAlex reports 33 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Principal component analysis (PCA) is a widespread exploratory data analysis tool. Sparse principal component analysis (SPCA) is a method that improves upon PCA by increasing the number of zeros in the loading vectors of PCA results. This makes the results more understandable and more usable. This bachelor's thesis introduces both methods, and includes examples using both real-world data and artifcial data. Also, the behavior of PCA under departure from weakly stationary data is explored.

Key concepts: Principal component analysis, Sparse PCA, Exploratory data analysis, Pattern recognition (psychology), Computer science, USable, Component (thermodynamics), Functional principal component analysis

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