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| 1 | A generalized power iteration method for solving quadratic problem on the Stiefel manifold显示文摘In this paper, we first propose a novel generalized power iteration(GPI) method to solve the quadratic problem on the Stiefel manifold(QPSM) as minWTW =ITr(WTAW-2WTB) along with the theoretical analysis. Accordingly, its special case known as the orthogonal least square regression(OLSR) is under further investigation. Based on the aforementioned studies, we then majorly focus on solving the unbalanced orthogonal procrustes problem(UOPP). As a result, not only a general convergent algorithm is derived theoretically but the efficiency of the proposed approach is verified empirically as well. | Feiping NIE Rui ZHANG Xuelong LI | 2017 | Science China(Information Sciences)2017，60，11： | 11 |
| 2 | Semi-supervised dimension reduction using trace ratio criterion显示文摘 | HUANG Heng XU Dong NIE Feiping | 2012 | IEEE Transactions on Neural Network Learning System2012，23，3： | 1 |
| 3 | A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback显示文摘 | Yi Yang Feiping Nie Dong Xu | 2012 | IEEE transactions on pattern analysis and machine intelligence2012，34，4： | 1 |
| 4 | Learning a Mahalanobis distance metric for data clustering and classifica- tion显示文摘 | Xiang Shiming Nie Feiping Zhang Changshui | 2008 | Pattern Recognition2008，41，12： | 1 |
| 5 | Learning a Ma- halanobis Distance Metric for Data Clustering and Classi- fication显示文摘 | Xiang Shiming Nie Feiping Zhang Changshui | 2008 | Pattern Recognition2008，41，12： | 1 |
| 6 | A unified framework for semi-supervised dimensionality reduction显示文摘 | Yangqiu Song Feiping Nie Changshui Zhang Shiming Xiang | 2008 | Pattern Recognition2008，，9： | 1 |
| 7 | Semi-supervised sub-manifold discriminant analysis显示文摘 | Yangqiu Song Feiping Nie Changshui Zhang | 2008 | Pattern Recognition Letters2008，，13： | 1 |
| 8 | Learning a Mahalanobis Distance Metric for Data Clustering and Classification显示文摘 | Xiang Shiming Nie Feiping Zhang Changshui | | 0，，12： | 1 |
| 9 | 3d Human Pose Recovery from Image by Efficient Visual Feature Selection 显示文摘 | Chen Cheng Yang Yi Nie Feiping | 2011 | Computer Vision and Image Understanding2011，115，3： | 1 |
| 10 | Semi- supervised Sub-manifold Discriminant Analysis 显示文摘 | Song Yangqiu Nie Feiping Zhang Changshui | 2008 | Pattern Recognition Letters2008，29，13： | 1 |
| 11 | Nonlinear dimensional- iN reduction with local spline embedding 显示文摘 | X1ANG SHIMING NIE FEIPING ZHANG CHANGSHUI | 2009 | IEEE Transactions on Knowledge and Data Engineering2009，21，9： | 1 |
| 12 | Learning a Mahalanobis distance metric for data clustering a classification显示文摘 | XIANG Shiming NIE Feiping ZHANG Changshui | 2008 | Pattern Recognition2008，41，12： | 1 |
| 13 | Discriminative least squares regression for multiclass classification and feature selection显示文摘 | Xiang Shiming Nie Feiping Meng Gaofeng Pan Chunhong Zhang Changshui | | 0，，： | 1 |
| 14 | Semi- supervised classification via local spline regression 显示文摘 | XIANG SHIMING NIE FEIPING ZHANG CHANGSHUI | 2010 | IEEETransactions on Pattern Analysis and Machine Intelligence2010，32，11： | 1 |
| 15 | Ranking with Adaptive Neighbors显示文摘Retrieving the most similar objects in a large-scale database for a given query is a fundamental building block in many application domains, ranging from web searches, visual, cross media, to document retrievals. Stateof-the-art approaches have mainly focused on capturing the underlying geometry of the data manifolds. Graphbased approaches, in particular, define various diffusion processes on weighted data graphs. Despite success,these approaches rely on fixed-weight graphs, making ranking sensitive to the input affinity matrix. In this study,we propose a new ranking algorithm that simultaneously learns the data affinity matrix and the ranking scores.The proposed optimization formulation assigns adaptive neighbors to each point in the data based on the local connectivity, and the smoothness constraint assigns similar ranking scores to similar data points. We develop a novel and efficient algorithm to solve the optimization problem. Evaluations using synthetic and real datasets suggest that the proposed algorithm can outperform the existing methods. | Muge Li Liangyue Li Feiping Nie | 2017 | Tsinghua Science and Technology2017，22，6： | 1 |
| 16 | Regression reformulations of LLE and LTSA with locally linear transformation显示文摘 | XIANG Shiming NIE Feiping | 2011 | IEEE Trans Actions on Systems2011，41，5： | 1 |
| 17 | Learning a Ma- halanobis distance metric for data clustering and classification 显示文摘 | Xiang Shiming Nie Feiping Zhang Changshui | 2008 | Pattern Recognition2008，41，12： | 1 |
| 18 | Eearning a Mahalanobis distance metric for data clustering and classification显示文摘 | Xiang Shiming Nie Feiping Zhang Changshui | 2008 | Pattern Recognition2008，41，12： | 1 |
| 19 | Semi-supervised sub-manifold discriminant analysis 显示文摘 | Song Yangqiu Nie Feiping Zhang Changshui | 2008 | Pattern Recognition Letters2008，29，13： | 1 |
| 20 | Embedding new data points for manifold learning via coordinate propagation 显示文摘 | Xiang Shiming Nie Feiping Song Yangqiu | 2009 | Knowledge and Information Systems2009，19，2： | 1 |
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