本課程已於 2017-09-27停開

106年第1學期-6196 高維度資料分析 課程資訊

課程分享

選課分析

本課程名額為 70人,已有1人選讀,尚餘名額69人。

評分方式

評分項目 配分比例 說明

授課教師

呂恒輝

教育目標

學習多變量降維方法於統計實務的應用分析。本課程介紹多個創新的統計降維方法(例如:分段逆迴歸法(Sliced Inverse Regression)、主要黑森定向法(principal Hessian Directions)等),用以達到資料縮減(data reduction)的目的,並且應用至大量資料集。 A Tentative Course Outline : Week 1 : 迴歸分析之維度縮減模型;主成份分析(PCA) Week 2-3 : 分段逆迴歸法 (SIR)及其應用 Week 4-5 : 主要黑森定向法 (PHD) Week 6 : 非線性擾動迴歸 (Nonlinear Confounding) Week 7 : 測量誤差 (Error in Regressors) Week 8 : 變異數之因子分析 (ANOVA Factorial Analysis) Week 9 : 期中報告 Week 10-11 : 設限迴歸 (Censored Regression) Week 12 : 樹狀迴歸 (Tree-structured Regression) Week 13-14 : 多變項反應變數迴歸 (Multivariate Outcome Data) Week 15-16 : 廣義線性區別分析 (Generalization Fisher LDA) Week 17 : 手寫辨識 (Handwritten digit Recognition) Week 18 : 期末報告

課程概述

The reduction of dimension is an issue that can arise in every scientific field. Generally speaking, the difficulty lies on how to visualize a high dimensional function or data set. People often ask: How do they look?, What structures are there?, What model should be used? Aside from the differences that underlie the various scientific contexts, such kinds of questions do have a common root in Statistics. This is the driving force for the study of high dimensional data analysis. This course will discuss several statistical methodologies useful for exploring voluminous data. They include principal component analysis, clustering and classification, survival analysis and other recent developed sufficient dimension reduction (SDR) methods. Sliced inverse regression (SIR) and principal Hessian direction (PHD) are two novel SDR methods, useful for the extraction of geometric information underlying noisy data of several dimensions. The theories of several SDR methods will be discussed in depth. They will be used as the backbone for the entire course. Examples from various application areas will be given. They include social/economic problems like unemployment rates, biostatistics problems like clinic trials with censoring, machine learning problems like handwritten digital recognition, biomedical problems like functional Magnet Resonance Imaging, and bioinformatics problems like micro-array gene expression etc.

課程資訊

參考書目

No textbook. Lecture notes and selected papers will be available.