Print Email Facebook Twitter Sparse principal component analysis Title Sparse principal component analysis Author Dannenberg, F.G.W. Contributor Van der Meulen, F.H. (mentor) Faculty Electrical Engineering, Mathematics and Computer Science Department Applied mathematics Programme Bsc Date 2010-09-08 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. Subject factor analysisprincipal component analysissparse principal component analysis To reference this document use: http://resolver.tudelft.nl/uuid:f2ae3f95-87a9-47ab-8fe1-73454c44b2c9 Embargo date 2010-09-09 Part of collection Student theses Document type bachelor thesis Rights (c) 2010 Dannenberg, F.G.W. Files PDF Bsc_verslag_fgw_dannenberg.pdf 750.44 KB Close viewer /islandora/object/uuid:f2ae3f95-87a9-47ab-8fe1-73454c44b2c9/datastream/OBJ/view