南京航空航天大学学报（英文版）
南京航空航天大學學報（英文版）
남경항공항천대학학보（영문판）
TRANSACTIONS OF NANJING UNIVERSITY OF AERONATICS & ASTRONAUTICS
2010年
3期
261-268
,共8页
图方法%拉普拉斯变换%无监督学习%维数约简%局部保持映射
圖方法%拉普拉斯變換%無鑑督學習%維數約簡%跼部保持映射
도방법%랍보랍사변환%무감독학습%유수약간%국부보지영사
graphic methods%Laplacian transforms%unsupervised learning%dimensionality reduction%locality preserving projection
局部保持投影(LPP)是一种典型的降维方法,通过保持数据的内在几何结构,LPP能够获得潜在的判别能力.然而,传统LPP的性能取决于人工预定义的近邻图,并且严重依赖于最近邻标准在原始数据空间中的性能.因此本文提出了一种新的降维算法--自助型局部保持投影(sdLPP).该方法首先执行LPP获得投影方向,然后在其变换的空间更新近邻图,并重复LPP.另外,本文还提出了一种改进的拉普拉斯打分(Laplacian score)标准作为算法迭代终止和判别力的参考.最后,在几个公共的UCI和人脸数据集上验证了该方法的有效性.
跼部保持投影(LPP)是一種典型的降維方法,通過保持數據的內在幾何結構,LPP能夠穫得潛在的判彆能力.然而,傳統LPP的性能取決于人工預定義的近鄰圖,併且嚴重依賴于最近鄰標準在原始數據空間中的性能.因此本文提齣瞭一種新的降維算法--自助型跼部保持投影(sdLPP).該方法首先執行LPP穫得投影方嚮,然後在其變換的空間更新近鄰圖,併重複LPP.另外,本文還提齣瞭一種改進的拉普拉斯打分(Laplacian score)標準作為算法迭代終止和判彆力的參攷.最後,在幾箇公共的UCI和人臉數據集上驗證瞭該方法的有效性.
국부보지투영(LPP)시일충전형적강유방법,통과보지수거적내재궤하결구,LPP능구획득잠재적판별능력.연이,전통LPP적성능취결우인공예정의적근린도,병차엄중의뢰우최근린표준재원시수거공간중적성능.인차본문제출료일충신적강유산법--자조형국부보지투영(sdLPP).해방법수선집행LPP획득투영방향,연후재기변환적공간경신근린도,병중복LPP.령외,본문환제출료일충개진적랍보랍사타분(Laplacian score)표준작위산법질대종지화판별력적삼고.최후,재궤개공공적UCI화인검수거집상험증료해방법적유효성.
Locality preserving projection (LPP) is a typical and popular dimensionality reduction (DR) method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data.However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space.To address this issue,a novel DR algorithm,called the self-dependent LPP (sdLPP) is proposed.And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space.Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP.Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination.Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.
