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    Title: JSDRlib : 一個充分維度縮減法的Java函式庫
    Other Titles: JSDRlib : a java library for sufficient dimension reduction
    Authors: 胡肇元;Hu, Jhao-Yuan
    Contributors: 淡江大學數學學系碩士班
    吳漢銘;Wu, Han-Ming
    Keywords: 維度縮減;Java語言;切片逆迴歸法;分類法;dimension reduction;Java language;Sliced inverse regression;classification
    Date: 2012
    Issue Date: 2013-04-13 11:11:02 (UTC+8)
    Abstract: 充分維度縮減法 (sufficient dimension reduction, SDR) 可以找出有效的維度縮減方向來探索高維度資料的內在結構。本論文以 Java 程式語言開發一個充分維度縮減法的函式庫,稱做jSDRlib,實作了SIR、SAVE、pHd、及 IRE 等等估計中央子空間 (central subspace) 的方法;同時提供了相關的卡方檢定來判定維度縮減個數。我們的目的在利用 Java 語言跨平台的特性,提供使用者一個具擴充性的維度縮減資料分析工具。應用所開發的函式庫,我們比較了不同充分維度縮減法在分類問題上的表現。進一步,jSDRlib 會和現存的二個充分維度縮減法工具相比較:dr 套件 (R 軟體) 及LDR 工具箱 (Matlab 軟體)。此外,本論文也提供一個使用者介面jSDRgui,讓維度縮減後的資料觀察更具方便性。jSDRlib 函式庫相關的使用說明與應用範例,請瀏覽 http://www.hmwu.idv.tw/jSDRlib。
    Sufficient dimension reduction (SDR) techniques aim to find the effective dimension reduction directions for exploring the intrinsic structure of high-dimensional datasets. In this study, we developed a Java library for performing sufficient dimension reduction techniques, namely jSDRlib. It implements SIR, SAVE, pHd, and IRE for estimating the central subspace. It also provides the estimation of the number of the effective dimensions via the statistical tests. Our purpose is to provide users an extensible tool for data analysis by taking advantage of the cross-platform feature of Java. We used jSDRlib to compare the performance of various sufficient dimension reduction methods to classification problems. Moreover, we compared our library with two existing SDR packages, the dr package in R and the LDR toolbox in Matlab . Finally, we developed a graphical user interface (GUI), based on jSDRlib, to investigate the dimension-reduced data. The user’s manual and the application examples are available at http://www.hmwu.idv.tw/jSDRlib.
    Appears in Collections:[數學學系暨研究所] 學位論文

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