Statistical Inference from High Dimensional Data -

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Résumé :
? Real-world problems can be high-dimensional, complex, and noisy ? More data does not imply more information ? Different approaches deal with the so-called curse of dimensionality to reduce irrelevant information ? A process with multidimensional information is not necessarily easy to interpret nor process ? In some real-world applications, the number of elements of a class is clearly lower than the other. The models tend to assume that the importance of the analysis belongs to the majority class and this is not usually the truth ? The analysis of complex diseases such as cancer are focused on more-than-one dimensional omic data ? The increasing amount of data thanks to the reduction of cost of the high-throughput experiments opens up a new era for integrative data-driven approaches ? Entropy-based approaches are of interest to reduce the dimensionality of high-dimensional data...

Sommaire:
? Real-world problems can be high-dimensional, complex, and noisy ? More data does not imply more information ? Different approaches deal with the so-called curse of dimensionality to reduce irrelevant information ? A process with multidimensional information is not necessarily easy to interpret nor process ? In some real-world applications, the number of elements of a class is clearly lower than the other. The models tend to assume that the importance of the analysis belongs to the majority class and this is not usually the truth ? The analysis of complex diseases such as cancer are focused on more-than-one dimensional omic data ? The increasing amount of data thanks to the reduction of cost of the high-throughput experiments opens up a new era for integrative data-driven approaches ? Entropy-based approaches are of interest to reduce the dimensionality of high-dimensional data...