High-Dimensional Statistical Learning: Roots, Justifications, and Potential Machineries
نویسندگان
چکیده
منابع مشابه
High-Dimensional Statistical Learning: Roots, Justifications, and Potential Machineries
High-dimensional data generally refer to data in which the number of variables is larger than the sample size. Analyzing such datasets poses great challenges for classical statistical learning because the finite-sample performance of methods developed within classical statistical learning does not live up to classical asymptotic premises in which the sample size unboundedly grows for a fixed di...
متن کاملStatistical Learning and High-dimensional Data
PREFACE This set of notes are used for teaching class " Statistical Learning and High-dimensional Data ". The goal is to give a comprehensive review on statistical learning methods and the methods for handling high-dimensional data. Currently, these appear to be hot areas in statistical fields so I hope that through this course, students can have a sense of what are their foundations and what a...
متن کاملStatistical Learning Methods for High Dimensional Genomic Data Statistical Learning Methods for High Dimensional Genomic Data Title: Statistical Learning Methods for High Dimensional Genomic Data
Due to their high-dimensionality, -omics technologies require the development of computational methods that are able to work with large number of variables. Each data type is characterized by its method of measurement and by the biological aspect under study. Understanding the data properties allows the design of sophisticated and effective computational models that are able to uncover and expl...
متن کاملContributions to high dimensional statistical learning
This report summarizes my contributions to high dimensional learning. Four research topics are addressed: Unsupervised nonlinear dimension reduction, high dimensional classification, high dimensional regression and copulas construction.
متن کاملHigh-Dimensional Unsupervised Active Learning Method
In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Cancer Informatics
سال: 2015
ISSN: 1176-9351,1176-9351
DOI: 10.4137/cin.s30804