Search Results - "Nie, Feiping"

Multiview Consensus Graph Clustering by Zhan, Kun, Nie, Feiping, Wang, Jing, Yang, Yi

“…A graph is usually formed to reveal the relationship between data points and graph structure is encoded by the affinity matrix. Most graph-based multiview…”
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Detecting Coherent Groups in Crowd Scenes by Multiview Clustering by Wang, Qi, Chen, Mulin, Nie, Feiping, Li, Xuelong

“…Detecting coherent groups is fundamentally important for crowd behavior analysis. In the past few decades, plenty of works have been conducted on this topic,…”
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Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification by Nie, Feiping, Cai, Guohao, Li, Jing, Li, Xuelong

“…Due to the efficiency of learning relationships and complex structures hidden in data, graph-oriented methods have been widely investigated and achieve…”
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Robust Object Co-Segmentation Using Background Prior by Han, Junwei, Quan, Rong, Zhang, Dingwen, Nie, Feiping

“…Given a set of images that contain objects from a common category, object co-segmentation aims at automatically discovering and segmenting such common objects…”
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Revisiting Co-Saliency Detection: A Novel Approach Based on Two-Stage Multi-View Spectral Rotation Co-clustering by Yao, Xiwen, Han, Junwei, Zhang, Dingwen, Nie, Feiping

“…With the goal of discovering the common and salient objects from the given image group, co-saliency detection has received tremendous research interest in…”
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Beyond Trace Ratio: Weighted Harmonic Mean of Trace Ratios for Multiclass Discriminant Analysis by Li, Zhihui, Nie, Feiping, Chang, Xiaojun, Yang, Yi

“…Linear discriminant analysis (LDA) is one of the most important supervised linear dimensional reduction techniques which seeks to learn low-dimensional…”
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Re-Weighted Discriminatively Embedded K -Means for Multi-View Clustering by Xu, Jinglin, Han, Junwei, Nie, Feiping, Li, Xuelong

“…Recent years, more and more multi-view data are widely used in many real-world applications. This kind of data (such as image data) is high dimensional and…”
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Multi-View Unsupervised Feature Selection with Adaptive Similarity and View Weight by Hou, Chenping, Nie, Feiping, Tao, Hong, Yi, Dongyun

“…With the advent of multi-view data, multi-view learning has become an important research direction in both machine learning and data mining. Considering the…”
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A New Formulation of Linear Discriminant Analysis for Robust Dimensionality Reduction by Zhao, Haifeng, Wang, Zheng, Nie, Feiping

“…Dimensionality reduction is a critical technology in the domain of pattern recognition, and linear discriminant analysis (LDA) is one of the most popular…”
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A generalized power iteration method for solving quadratic problem on the Stiefel manifold by Nie, Feiping, Zhang, Rui, Li, Xuelong

“…In this paper, we first propose a novel generalized power iteration （GPI） method to solve the quadratic problem on the Stiefel manifold （QPSM） as minwww=I…”
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Discriminative Least Squares Regression for Multiclass Classification and Feature Selection by Xiang, Shiming, Nie, Feiping, Meng, Gaofeng, Pan, Chunhong, Zhang, Changshui

“…This paper presents a framework of discriminative least squares regression (LSR) for multiclass classification and feature selection. The core idea is to…”
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Feature Selection via Global Redundancy Minimization by Wang, De, Nie, Feiping, Huang, Heng

“…Feature selection has been an important research topic in data mining, because the real data sets often have high-dimensional features, such as the…”
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Multi-view Subspace Clustering by Gao, Hongchang, Nie, Feiping, Li, Xuelong, Huang, Heng

“…For many computer vision applications, the data sets distribute on certain low-dimensional subspaces. Subspace clustering is to find such underlying subspaces…”
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Fast Spectral Clustering With Anchor Graph for Large Hyperspectral Images by Wang, Rong, Nie, Feiping, Yu, Weizhong

“…The large-scale hyperspectral image (HSI) clustering problem has attracted significant attention in the field of remote sensing. Most traditional graph-based…”
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A General Framework for Auto-Weighted Feature Selection via Global Redundancy Minimization by Nie, Feiping, Yang, Sheng, Zhang, Rui, Li, Xuelong

“…Most existing feature selection methods rank all the features by a certain criterion via which the top ranking features are selected for the subsequent…”
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Joint Embedding Learning and Sparse Regression: A Framework for Unsupervised Feature Selection by Hou, Chenping, Nie, Feiping, Li, Xuelong, Yi, Dongyun, Wu, Yi

“…Feature selection has aroused considerable research interests during the last few decades. Traditional learning-based feature selection methods separate…”
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Discriminative Embedded Clustering: A Framework for Grouping High-Dimensional Data by Hou, Chenping, Nie, Feiping, Yi, Dongyun, Tao, Dacheng

“…In many real applications of machine learning and data mining, we are often confronted with high-dimensional data. How to cluster high-dimensional data is…”
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Matrix Completion Based on Non-Convex Low-Rank Approximation by Nie, Feiping, Hu, Zhanxuan, Li, Xuelong

“…Without any prior structure information, nuclear norm minimization (NNM), a convex relaxation for rank minimization (RM), is a widespread tool for matrix…”
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Compound Rank- k Projections for Bilinear Analysis by Chang, Xiaojun, Nie, Feiping, Wang, Sen, Yang, Yi, Zhou, Xiaofang, Zhang, Chengqi

“…In many real-world applications, data are represented by matrices or high-order tensors. Despite the promising performance, the existing 2-D discriminant…”
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Fast and Orthogonal Locality Preserving Projections for Dimensionality Reduction by Wang, Rong, Nie, Feiping, Hong, Richang, Chang, Xiaojun, Yang, Xiaojun, Yu, Weizhong

“…The locality preserving projections (LPP) algorithm is a recently developed linear dimensionality reduction algorithm that has been frequently used in face…”
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