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Non-parametric inference for functional data over two-dimensional domains entails additional computational and statistical challenges, compared to the one-dimensional case. Separability of the covariance is commonly assumed to address these issues in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c6c182a3580d6fe252b5ee7c65d55bd
Autor:
Tomáš Masák
Publikováno v:
Statistika: Statistics and Economy Journal, Vol 97, Iss 3, Pp 88-106 (2017)
Principal component analysis (PCA) is a popular dimensionality reduction and data visualization method. Sparse PCA (SPCA) is its extensively studied and NP-hard-to-solve modifcation. In the past decade, many diferent algorithms were proposed to per
Externí odkaz:
https://doaj.org/article/77688b5e412c4fb8a0e4b7f2c82d1563
