no code implementations • 25 Apr 2024 • Haorui Xiang, Zhichang Wu, Guoxu Li, Rong Wang, Feiping Nie, Xuelong Li
Adhering to this concept, we introduce a new model, Capped $\ell_{p}$-Norm Support Vector Ordinal Regression(CSVOR), that is robust to outliers.
1 code implementation • 8 Mar 2024 • Danyang Wu, Xinjie Shen, Jitao Lu, Jin Xu, Feiping Nie
Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view polynomial operators.
no code implementations • 1 Mar 2024 • Xueyuan Xu, Fulin Wei, Tianyuan Jia, Li Zhuo, Feiping Nie, Xia Wu
Additionally, both global feature redundancy and global label relevancy information have been considered in the orthogonal regression model, which could contribute to the search for discriminative and non-redundant feature subsets in the multi-label data.
1 code implementation • 11 Dec 2023 • Feiping Nie, Zhezheng Hao, Rong Wang
Support Vector Machine (SVM) stands out as a prominent machine learning technique widely applied in practical pattern recognition tasks.
no code implementations • 26 Nov 2023 • Feiping Nie, Jitao Lu, Danyang Wu, Rong Wang, Xuelong Li
To address the problems, we propose a novel N-Cut solver designed based on the famous coordinate descent method.
no code implementations • 2 Oct 2023 • Xinjie Shen, Danyang Wu, Jitao Lu, Junjie Liang, Jin Xu, Feiping Nie
Moreover, applications of pseudo labels in graph neural networks (GNNs) oversee the difference between graph learning and other machine learning tasks such as message passing mechanism.
no code implementations • 12 May 2023 • Bo Jiang, Fei Xu, Ziyan Zhang, Jin Tang, Feiping Nie
To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning.
no code implementations • 7 Dec 2022 • Feiping Nie, Hong Chen, Rong Wang, Xuelong Li
This paper presents an algorithm to solve the Soft k-Means problem globally.
no code implementations • 3 Nov 2022 • Liangchen Liu, Qiuhong Ke, Chaojie Li, Feiping Nie, Yingying Zhu
In this paper, we formulate a novel clustering model, which exploits the non-negative feature property and, more importantly, incorporates the multi-view information into a unified joint learning framework: the unified multi-view orthonormal non-negative graph based clustering framework (Umv-ONGC).
no code implementations • 11 Apr 2022 • Junyun Cui, Xiaoyu Shen, Feiping Nie, Zheng Wang, Jinglong Wang, Yulong Chen
In this paper, to address the current lack of comprehensive survey of existing LJP tasks, datasets, models and evaluations, (1) we analyze 31 LJP datasets in 6 languages, present their construction process and define a classification method of LJP with 3 different attributes; (2) we summarize 14 evaluation metrics under four categories for different outputs of LJP tasks; (3) we review 12 legal-domain pretrained models in 3 languages and highlight 3 major research directions for LJP; (4) we show the state-of-art results for 8 representative datasets from different court cases and discuss the open challenges.
no code implementations • 31 Jan 2022 • Peican Zhu, Xin Hou, Keke Tang, Zhen Wang, Feiping Nie
For feature engineering, feature selection seems to be an important research content in which is anticipated to select "excellent" features from candidate ones.
no code implementations • 20 Jan 2022 • Kun Song, Junwei Han, Gong Cheng, Jiwen Lu, Feiping Nie
In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining.
no code implementations • 9 Dec 2021 • Wei Chang, Feiping Nie, Rong Wang, Xuelong Li
Multi-task learning has been observed by many researchers, which supposes that different tasks can share a low-rank common yet latent subspace.
no code implementations • NeurIPS 2021 • Feiping Nie, Shenfei Pei, Rong Wang, Liang Zhang, Jun Wu, Qinglong Chang, Xuelong Li
We also developed a general model that unified LKM, KSUMS, and SC, and discussed the connection among them.
no code implementations • 6 Jan 2021 • Rong Wang, Yihang Lu, Qianrong Zhang, Feiping Nie, Zhen Wang, Xuelong Li
To alleviate this problem, we proposed a novel ensemble and random collaborative representation-based detector (ERCRD) for HAD, which comprises two closely related stages.
no code implementations • 29 Dec 2020 • Zhengxin Li, Feiping Nie, Jintang Bian, Xuelong Li
However, real-world data contain a large number of noise samples and features, making the similarity matrix constructed by original data cannot be completely reliable.
1 code implementation • NeurIPS 2020 • Shenfei Pei, Feiping Nie, Rong Wang, Xuelong Li
In particular, over 15x and 7x speed-up can be obtained with respect to $k$-means on the synthetic dataset of 1 million samples and the benchmark dataset (CelebA) of 200k samples, respectively [GitHub].
1 code implementation • NeurIPS 2020 • Lai Tian, Feiping Nie, Rong Wang, Xuelong Li
This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously.
no code implementations • 24 Sep 2020 • Caixia Yan, Xiaojun Chang, Minnan Luo, Qinghua Zheng, Xiaoqin Zhang, Zhihui Li, Feiping Nie
In this regard, a novel self-weighted robust LDA with l21-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes.
no code implementations • 15 Jun 2020 • Xuesong Wang, Lina Yao, Xianzhi Wang, Feiping Nie
Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations.
no code implementations • 12 May 2020 • Zhe Liu, Yun Li, Lina Yao, Xianzhi Wang, Feiping Nie
Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews.
no code implementations • 26 Jan 2020 • Di Hu, Zheng Wang, Haoyi Xiong, Dong Wang, Feiping Nie, Dejing Dou
Associating sound and its producer in complex audiovisual scene is a challenging task, especially when we are lack of annotated training data.
no code implementations • 9 Oct 2019 • Xia Wu, Xueyuan Xu, Jianhong Liu, Hailing Wang, Bin Hu, Feiping Nie
Effective features can improve the performance of a model, which can thus help us understand the characteristics and underlying structure of complex data.
no code implementations • 25 Sep 2019 • Saad Elbeleidy, Lyujian Lu, L. Zoe Baker, Hua Wang, Feiping Nie
Longitudinal data is often available inconsistently across individuals resulting in ignoring of additionally available data.
no code implementations • 19 Aug 2019 • Jinglin Xu, Junwei Han, Mingliang Xu, Feiping Nie, Xuelong. Li
Clustering is an effective technique in data mining to group a set of objects in terms of some attributes.
no code implementations • 2 Jul 2019 • Feiping Nie, Zhanxuan Hu, Xiaoqian Wang, Rong Wang, Xuelong. Li, Heng Huang
This work aims at solving the problems with intractable sparsity-inducing norms that are often encountered in various machine learning tasks, such as multi-task learning, subspace clustering, feature selection, robust principal component analysis, and so on.
no code implementations • 29 Jun 2019 • Feiping Nie, Hua Wang, Zheng Wang, Heng Huang
In this paper, we propose a novel robust linear discriminant analysis method based on the L1, 2-norm ratio minimization.
no code implementations • 21 Jun 2019 • Feiping Nie, Jing Li, Xuelong. Li
Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering.
no code implementations • 23 Apr 2019 • Lai Tian, Feiping Nie, Xuelong. Li
This paper presents new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA), which performs feature selection and PCA simultaneously.
no code implementations • CVPR 2019 • Di Hu, Dong Wang, Xuelong. Li, Feiping Nie, Qi. Wang
different encoding schemes indicate that using machine model to accelerate optimization evaluation and reduce experimental cost is feasible to some extent, which could dramatically promote the upgrading of encoding scheme then help the blind to improve their visual perception ability.
no code implementations • 8 Apr 2019 • Fei Wang, Zhongheng Li, Fang He, Rong Wang, Weizhong Yu, Feiping Nie
We explain the rationality of this and illustrate the theorem that when the dimension of these features increases, the performance of SVM would not be worse, which can explain the resistant to overfitting of AdaBoost.
no code implementations • 8 Oct 2018 • Di Hu, Feiping Nie, Xuelong. Li
Hence, it is highly expected to learn effective joint representation by fusing the features of different modalities.
no code implementations • 8 Oct 2018 • Di Hu, Feiping Nie, Xuelong. Li
The conventional supervised hashing methods based on classification do not entirely meet the requirements of hashing technique, but Linear Discriminant Analysis (LDA) does.
no code implementations • 14 Aug 2018 • Zhanxuan Hu, Feiping Nie, Rong Wang, Xuelong Li
Low rank regularization, in essence, involves introducing a low rank or approximately low rank assumption for matrix we aim to learn, which has achieved great success in many fields including machine learning, data mining and computer version.
1 code implementation • CVPR 2019 • Di Hu, Feiping Nie, Xuelong. Li
And such integrated multimodal clustering network can be effectively trained with max-margin loss in the end-to-end fashion.
no code implementations • CVPR 2018 • Kai Liu, Hua Wang, Feiping Nie, Hao Zhang
To tackle these two challenges, in this paper we propose a novel image representation learning method that can integrate the local patches (the instances) of an input image (the bag) and its holistic representation into one single-vector representation.
no code implementations • 13 Apr 2018 • Shuai Zheng, Chris Ding, Feiping Nie
Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA).
no code implementations • 14 Mar 2018 • Muge Li, Liangyue Li, Feiping Nie
Despite success, these approaches rely on fixed-weight graphs, making ranking sensitive to the input affinity matrix.
no code implementations • NeurIPS 2017 • Feiping Nie, Xiaoqian Wang, Cheng Deng, Heng Huang
In graph based co-clustering methods, a bipartite graph is constructed to depict the relation between features and samples.
no code implementations • ICCV 2017 • Xiaojun Chen, Joshua Zhexue Haung, Feiping Nie, Renjie Chen, Qingyao Wu
In the new method, a self-balanced min-cut model is proposed in which the Exclusive Lasso is implicitly introduced as a balance regularizer in order to produce balanced partition.
no code implementations • 9 Sep 2017 • Yanwei Pang, Bo Zhou, Feiping Nie
It is interesting that the optimal regularization parameter is adaptive to the neighbors in low-dimensional space and has intuitive meaning.
1 code implementation • 17 Aug 2017 • Xuelong. Li, Di Hu, Feiping Nie
Based on the analysis, we provide a so-called Deep Binary Reconstruction (DBRC) network that can directly learn the binary hashing codes in an unsupervised fashion.
no code implementations • 3 May 2017 • Zan Gao, Guotai Zhang, Feiping Nie, Hua Zhang
Principal component analysis (PCA) is a traditional technique for unsupervised dimensionality reduction, which is often employed to seek a projection to best represent the data in a least-squares sense, but if the original data is nonlinear structure, the performance of PCA will quickly drop.
no code implementations • 20 Feb 2017 • Mengfan Tang, Feiping Nie, Siripen Pongpaichet, Ramesh Jain
Photos are becoming spontaneous, objective, and universal sources of information.
no code implementations • 14 Oct 2016 • Shuai Zheng, Feiping Nie, Chris Ding, Heng Huang
In null space based LDA (NLDA), a well-known LDA extension, between-class distance is maximized in the null space of the within-class scatter matrix.
no code implementations • 14 Oct 2016 • Shuai Zheng, Xiao Cai, Chris Ding, Feiping Nie, Heng Huang
Real life data often includes information from different channels.
no code implementations • 9 Jul 2016 • Sen Wang, Feiping Nie, Xiaojun Chang, Xue Li, Quan Z. Sheng, Lina Yao
We propose a method that utilizes both the manifold structure of data and local discriminant information.
no code implementations • CVPR 2016 • Rong Quan, Junwei Han, Dingwen Zhang, Feiping Nie
Aiming at automatically discovering the common objects contained in a set of relevant images and segmenting them as foreground simultaneously, object co-segmentation has become an active research topic in recent years.
no code implementations • CVPR 2016 • Jinglin Xu, Junwei Han, Feiping Nie
In real world applications, more and more data, for example, image/video data, are high dimensional and represented by multiple views which describe different perspectives of the data.
no code implementations • 28 Mar 2016 • Feiping Nie, Heng Huang
In this paper, we propose to maximize the L21-norm based robust PCA objective, which is theoretically connected to the minimization of reconstruction error.
no code implementations • ICCV 2015 • Hongchang Gao, Feiping Nie, Xuelong. Li, Heng Huang
In this paper, we propose a novel multi-view subspace clustering method.
no code implementations • 5 Sep 2015 • Wenhao Jiang, Cheng Deng, Wei Liu, Feiping Nie, Fu-Lai Chung, Heng Huang
Domain adaptation problems arise in a variety of applications, where a training dataset from the \textit{source} domain and a test dataset from the \textit{target} domain typically follow different distributions.
no code implementations • 3 Jun 2015 • Sen Wang, Feiping Nie, Xiaojun Chang, Lina Yao, Xue Li, Quan Z. Sheng
In this paper, we propose an unsupervised feature selection method seeking a feature coefficient matrix to select the most distinctive features.
no code implementations • CVPR 2015 • Lianli Gao, Jingkuan Song, Feiping Nie, Yan Yan, Nicu Sebe, Heng Tao Shen
In multimedia annotation, due to the time constraints and the tediousness of manual tagging, it is quite common to utilize both tagged and untagged data to improve the performance of supervised learning when only limited tagged training data are available.
no code implementations • 21 Apr 2015 • Hong Tao, Chenping Hou, Feiping Nie, Yuanyuan Jiao, Dongyun Yi
In this paper, a feature selection method is proposed by combining the popular transformation based dimensionality reduction method Linear Discriminant Analysis (LDA) and sparsity regularization.
no code implementations • NeurIPS 2014 • Deguang Kong, Ryohei Fujimaki, Ji Liu, Feiping Nie, Chris Ding
Group lasso is widely used to enforce the structural sparsity, which achieves the sparsity at inter-group level.
no code implementations • 23 Nov 2014 • Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang, Xiaofang Zhou
Thus, applying manifold information obtained from the original space to the clustering process in a low-dimensional subspace is prone to inferior performance.
no code implementations • 23 Nov 2014 • Xiaojun Chang, Feiping Nie, Zhigang Ma, Yi Yang
Clustering is an effective technique in data mining to generate groups that are the matter of interest.
no code implementations • 23 Nov 2014 • Xiaojun Chang, Feiping Nie, Yi Yang, Heng Huang
In addition, based on the sparse model used in CSPCA, an optimal weight is assigned to each of the original feature, which in turn provides the output with good interpretability.
no code implementations • 23 Nov 2014 • Xiaojun Chang, Feiping Nie, Yi Yang, Heng Huang
Our algorithm is built upon two advancements of the state of the art:1) label propagation, which propagates a node\'s labels to neighboring nodes according to their proximity; and 2) manifold learning, which has been widely used in its capacity to leverage the manifold structure of data points.
no code implementations • 23 Nov 2014 • Xiaojun Chang, Feiping Nie, Sen Wang, Yi Yang, Xiaofang Zhou, Chengqi Zhang
In many real-world applications, data are represented by matrices or high-order tensors.
no code implementations • CVPR 2013 • Hua Wang, Feiping Nie, Heng Huang, Chris Ding
We applied our SMML method to five broadly used object categorization and scene understanding image data sets for both singlelabel and multi-label image classification tasks.
no code implementations • NeurIPS 2012 • Dijun Luo, Heng Huang, Feiping Nie, Chris H. Ding
In many graph-based machine learning and data mining approaches, the quality of the graph is critical.
no code implementations • NeurIPS 2012 • Hua Wang, Feiping Nie, Heng Huang, Jingwen Yan, Sungeun Kim, Shannon Risacher, Andrew Saykin, Li Shen
Alzheimer disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions.
no code implementations • NeurIPS 2010 • Feiping Nie, Heng Huang, Xiao Cai, Chris H. Ding
The ℓ2, 1-norm based loss function is robust to outliers in data points and the ℓ2, 1-norm regularization selects features across all data points with joint sparsity.