Active Learning Based on Locally Linear Propagation Reconstruction and Selection of Kernel Parameters for Support Vector Machines with General RBF Kernels: Gradient Descent-Based Approaches

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簡歷

基本資料

Project title

Code/計畫編號

MOST103-2221-E019-032-MY2

Translated Name/計畫中文名

使用梯度下降法於基於區域線性傳遞重建技巧的主動式學習演算法與挑選一般性徑向基底支撐向量機的核函數參數

Project Coordinator/計畫主持人

Chin-Chun Chang

Funding Organization/主管機關

National Science and Technology Council

Department/Unit

Department of Computer Science and Engineering

Website

https://www.grb.gov.tw/search/planDetail?id=8356718

Year

2014

Start date/計畫起

01-08-2014

Expected Completion/計畫迄

31-07-2015

Bugetid/研究經費

573千元

ResearchField/研究領域

資訊科學--軟體

主動式學習演算法選擇對監督式學習重要的樣本來標示。使用主動式學習演算法可以節省許多標示樣本的成本。非常多主動式學習演算法使用資料幾何結構與伴隨高斯核函數的再生核希爾伯特空間的好處於選擇查詢樣本。在使用那些演算法前，有關於高斯核函數與$k$-NN圖的參數必須先設定好。作為一個探索資料的工具，主動式學習演算法應該具備自動選取那些參數的能力。在這份報告裡，我們使用凸條件限制的區域線性嵌入(LLE)來學習資料在伴隨高斯核函數的再生核希爾伯特空間的幾何結構。在資料的幾何結構是區域且稀疏的假設下，我們提出可以自動選取適合的參數來學習資料的幾何結構的方法。另外，我們使用建立在LLE權重矩陣之馬可夫矩陣，提出根據由所有樣本至選擇的樣本隨機漫步路徑長度期望值作為重要樣本挑選準則。我們提出依據此準則的一個貪婪式演算法並證明找到的重要樣本集合與在此準則下最佳集合間的在此準則相對差異的上限。我們也提出一個兩階段方式來處理大資料集。經由數個規模從數百到數萬筆資料的資料集驗證後，證實我們提出的方法非常可行。"Active learning algorithms aim at selecting important samples to label for subsequent machine learning tasks. Many active learning algorithms leverage the geometrical structure of the data and the reproducing kernel Hilbert space (RKHS) induced by a Gaussian radial basis function (RBF) kernel for query-sample selection. Parameters for the kernel function and the $k$-nearest-neighborhood graph must be properly set beforehand. As a tool exploring the structure of data, active learning algorithms with automatic tuning of those parameters are desirable. In this report, local linear embedding (LLE) with convex constraints on neighbor weights is used to learn the geometrical structure of the data in the RKHS induced by a Gaussian RBF kernel. Automatic tuning of the kernel parameter is based on the assumption that the geometrical structure of the data in the RKHS is sparse and local. With the Markov matrix established based on the learned LLE weight matrix, the total expected path-length of the random walks from all samples to selected samples is proposed to be a criterion for query-sample selection. A greedy algorithm having a guaranteed solution bound is developed to select query samples and a two-phase scheme is also proposed for large-scale data sets. Experimental results on data sets including hundreds to tens of thousands of samples have shown the feasibility of the proposed approach."

Keyword(s)

主動式學習
區域線性傳遞式重建
RBF 支撐向量機
距離學習
Active learning
Locally linear propagation reconstruction
RBF support vector machines
Distance metric learning

DSpace CRIS

Active Learning Based on Locally Linear Propagation Reconstruction and Selection of Kernel Parameters for Support Vector Machines with General RBF Kernels: Gradient Descent-Based Approaches

基本資料

Description