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- W4238955835 abstract "In Chap. 2, SSA is normally considered as a model-free technique. The main body of Chap. 2 is devoted to careful description of Basic SSA, its main capabilities, choice of parameters and various indicators which help in recognizing good separability between different components and hence successful SSA decompositions. Several approaches for improving separability are examined; these approaches include rotations in chosen eigenspaces, the use of different matrix norms, as well as the use of prior and posterior information. Chapter 2 concludes with a description of multidimensional and multivariate extensions of SSA, which are applied to collections of time series and digital images respectively." @default.
- W4238955835 created "2022-05-12" @default.
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- W4238955835 date "2020-01-01" @default.
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- W4238955835 title "Basic SSA" @default.
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