电子学报
電子學報
전자학보
ACTA ELECTRONICA SINICA
2014年
11期
2198-2204
,共7页
基于图的半监督学习%稀疏表示%最近特征空间嵌入%正则化
基于圖的半鑑督學習%稀疏錶示%最近特徵空間嵌入%正則化
기우도적반감독학습%희소표시%최근특정공간감입%정칙화
graph based semi-supervised learning%sparse representation%nearest feature space embedding%regularization
在机器学习领域，半监督学习作为一种有力工具吸引了越来越多的关注，其利用少量带标签数据和大量无标签数据进行有效学习，其中基于图的半监督学习方法因其优雅的数学形式和良好的学习性能而引起更广泛的研究。针对现有基于图的半监督学习方法所存在的模型参数敏感和数据判别信息不充分等问题，提出一种稀疏特征空间嵌入正则化（Sparse Feature Space embedding Regularization ，SFSR ）半监督学习框架，其主要思想为：首先分别将原始数据嵌入到线性特征空间，然后利用特征空间嵌入投影点集来稀疏重构原始数据，随后在由原始数据线性张成的标签空间通过保留这种稀疏表示关系来构建一个Laplacian正则化项，或称SFSR ，最后提出一个鲁棒的基于SFSR的半监督学习框架，在几个实际基准数据库上的综合实验结果证实了所提框架的鲁棒有效性。
在機器學習領域，半鑑督學習作為一種有力工具吸引瞭越來越多的關註，其利用少量帶標籤數據和大量無標籤數據進行有效學習，其中基于圖的半鑑督學習方法因其優雅的數學形式和良好的學習性能而引起更廣汎的研究。針對現有基于圖的半鑑督學習方法所存在的模型參數敏感和數據判彆信息不充分等問題，提齣一種稀疏特徵空間嵌入正則化（Sparse Feature Space embedding Regularization ，SFSR ）半鑑督學習框架，其主要思想為：首先分彆將原始數據嵌入到線性特徵空間，然後利用特徵空間嵌入投影點集來稀疏重構原始數據，隨後在由原始數據線性張成的標籤空間通過保留這種稀疏錶示關繫來構建一箇Laplacian正則化項，或稱SFSR ，最後提齣一箇魯棒的基于SFSR的半鑑督學習框架，在幾箇實際基準數據庫上的綜閤實驗結果證實瞭所提框架的魯棒有效性。
재궤기학습영역，반감독학습작위일충유력공구흡인료월래월다적관주，기이용소량대표첨수거화대량무표첨수거진행유효학습，기중기우도적반감독학습방법인기우아적수학형식화량호적학습성능이인기경엄범적연구。침대현유기우도적반감독학습방법소존재적모형삼수민감화수거판별신식불충분등문제，제출일충희소특정공간감입정칙화（Sparse Feature Space embedding Regularization ，SFSR ）반감독학습광가，기주요사상위：수선분별장원시수거감입도선성특정공간，연후이용특정공간감입투영점집래희소중구원시수거，수후재유원시수거선성장성적표첨공간통과보류저충희소표시관계래구건일개Laplacian정칙화항，혹칭SFSR ，최후제출일개로봉적기우SFSR적반감독학습광가，재궤개실제기준수거고상적종합실험결과증실료소제광가적로봉유효성。
Semi-supervised learning(SSL) ,as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data ,has been attracting increasing attention in machine learning community .Of various SSL methods ,graph based approaches have attracted more extensive research due to their elegant mathematical formulation and good performance .How-ever ,there may exist several nontrivial concerns such as such as model parameters sensitiveness and insufficient discriminative infor-mation in data space ,etc ,in existing graph based SSL approaches .To these ends ,in this paper ,we propose a robust Sparse Feature Space embedding Regularization (SFSR )SSL framework .The main idea of the proposed SFSR includes three folds:(1 )linearly em-bedding input data into its feature spaces (2 )sparsely reconstructing input data using its feature space embedding projection images;and (3 )preserving the same sparse representation relationship among labels of data as that among data in some label space spanned linearly by input data ,thus constructing a novel sparse nearest feature space embedding regularizer ,coined as SFSR .The comprehen-sive experimental results on several real-world benchmark databases are presented to demonstrate the significantly robust effective-ness of our proposed method .
