Statistical Analysis of Complex Data
Dimensionality reduction and classification methods
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Statistical learning (SL) is the study of the generalizable extraction of knowledge from data (Friedman et al. 2001). The concept of learning is used when human expertise does not exist, humans are unable to explain their expertise, solution changes in time, solution needs to be adapted to particular cases. The principal algorithms used in SL are classified in: supervised learning (e.g. regression and classification), unsupervised learning (e.g. association and clustering), semi-supervised, it combines both labeled and unlabeled examples to generate an appropriate function or classifier. Follo...
Statistical learning (SL) is the study of the generalizable extraction of knowledge from data (Friedman et al. 2001). The concept of learning is used when human expertise does not exist, humans are unable to explain their expertise, solution changes in time, solution needs to be adapted to particular cases. The principal algorithms used in SL are classified in: supervised learning (e.g. regression and classification), unsupervised learning (e.g. association and clustering), semi-supervised, it combines both labeled and unlabeled examples to generate an appropriate function or classifier. Following this research idea, in this book we propose a good review on the more recent statistical models used to solve the dimensionality problem recently discussed.
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