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SAHDL: Sparse Attention Hypergraph Regularized Dictionary Learning [article]

Shuai Shao and Rui Xu and Yan-Jiang Wang and Weifeng Liu and Bao-Di Liu
2020 arXiv   pre-print
More specifically, we first construct a sparse attention hypergraph, asset attention weights to samples by employing the ℓ_1-norm sparse regularization to mine the high-order relationship among sample  ...  features.  ...  Sparse Attention Hypergraph Regularized Dictionary Learning (SAHDL) Objective Function We embed the hypergraph regularization term into the conventional dictionary learning method and form the objective  ... 
arXiv:2010.12416v1 fatcat:4qat227onjhcrpixpldfnpoq6i

Latent semantic learning with structured sparse representation for human action recognition

Zhiwu Lu, Yuxin Peng
2013 Pattern Recognition  
More importantly, we construct the L1-graph with structured sparse representation, which can be obtained by structured sparse coding with its structured sparsity ensured by novel L1-norm hypergraph regularization  ...  This paper proposes a novel latent semantic learning method for extracting high-level features (i.e. latent semantics) from a large vocabulary of abundant mid-level features (i.e. visual keywords) with  ...  To explore structured sparsity in L 1 -graph construction, we further improve the sparse coding algorithm with L 1norm hypergraph regularization.  ... 
doi:10.1016/j.patcog.2012.09.027 fatcat:tkjtzttzc5gfjhxvuirai4oz5m

Diagnosis of Alzheimer's Disease Using View-Aligned Hypergraph Learning with Incomplete Multi-modality Data [chapter]

Mingxia Liu, Jun Zhang, Pew-Thian Yap, Dinggang Shen
2016 Lecture Notes in Computer Science  
A view-aligned hypergraph classification (VAHC) model is then proposed, by using a viewaligned regularizer to model the view coherence.  ...  In this paper, we propose a viewaligned hypergraph learning (VAHL) method to explicitly model the coherence among the views.  ...  A larger value for the l 1 regularization parameter (i.e., ε) in SR will lead to more sparse coefficients.  ... 
doi:10.1007/978-3-319-46720-7_36 pmid:28066842 pmcid:PMC5207479 fatcat:nlhwmg7d2jhsplmexhwhvoyidm

View-aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi-modality data

Mingxia Liu, Jun Zhang, Pew-Thian Yap, Dinggang Shen
2017 Medical Image Analysis  
A view-aligned hypergraph classification (VAHC) model is then proposed, by using a view-aligned regularizer to capture coherence among views.  ...  In this paper, we propose a view-aligned hypergraph learning (VAHL) method to explicitly model the coherence among views.  ...  Sparse Representation based Hypergraph Construction In this study, AD/MCI diagnosis is formulated as a hypergraph based multi-view learning problem.  ... 
doi:10.1016/ pmid:27898305 pmcid:PMC5239753 fatcat:np65iu7jrjfjfcy4ex54ular3a

Joint hypergraph learning and sparse regression for feature selection

Zhihong Zhang, Lu Bai, Yuanheng Liang, Edwin Hancock
2017 Pattern Recognition  
Here on the other hand, we perform data structure learning and feature * Corresponding author  ...  In this paper, we propose a uniÞed framework for improved structure estimation and feature selection.  ...  This paper introduces a novel feature selection framework: joint hypergraph learning and sparse regression (referred to as JHLSR).  ... 
doi:10.1016/j.patcog.2016.06.009 fatcat:2pdq5verzzclvik3tapvf43sf4

Improved hypergraph regularized Nonnegative Matrix Factorization with sparse representation

Sheng Huang, Hongxing Wang, Yongxin Ge, Luwen Huangfu, Xiaohong Zhang, Dan Yang
2018 Pattern Recognition Letters  
In this paper, we leverage the sparse representation to construct a sparse hypergraph for better capturing the manifold structure of data, and then impose the sparse hypergraph as a regularization to the  ...  NMF framework to present a novel GNMF algorithm called Sparse Hypergraph regularized Nonnegative Matrix Factorization (SHNMF).  ...  In SHNMF, the sparse representation is leveraged to construct a sparse hypergraph for regularizing the NMF algorithm.  ... 
doi:10.1016/j.patrec.2017.11.017 fatcat:2qzwqztmyfdg3bipuzcsei2g6e

Combinative hypergraph learning for semi-supervised image classification

Binghui Wei, Ming Cheng, Cheng Wang, Jonathan Li
2015 Neurocomputing  
CHL captures the similarity between two samples in the same category by adding sparse hypergraph learning to conventional hypergraph learning.  ...  In order to extend the high-order relationship of samples, we incorporate linear correlation of sparse representation to hypergraph learning framework to improve learning performance.  ...  Sparse hypergraph learning Sparse hypergraph learning is similar to conventional hypergraph learning.  ... 
doi:10.1016/j.neucom.2014.11.028 fatcat:avkdded5xzdmdi53kwt6nshhou

Robust Sparse Coding via Self-Paced Learning [article]

Xiaodong Feng, Zhiwei Tang, Sen Wu
2017 arXiv   pre-print
We also generalize the self-paced learning schema into different levels of dynamic selection on samples, features and elements respectively.  ...  To enhance the learning robustness, in this paper, we propose a unified framework named Self-Paced Sparse Coding (SPSC), which gradually include matrix elements into SC learning from easy to complex.  ...  (hypergraph consistent sparse coding) integrates a hypergraph incidence consistency regularization term. • SPSC S , SPSC E and SPSC F .  ... 
arXiv:1709.03030v1 fatcat:pynr5z3e3nczfb7n4syfololuy

Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding

Hong Huang, Meili Chen, Yule Duan
2019 Remote Sensing  
A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification.  ...  SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph.  ...  SSRHE explores sparse coefficients and label information of pixels to adaptively select neighbors for constructing a regularized sparse intraclass hypergraph and a regularized sparse interclass hypergraph  ... 
doi:10.3390/rs11091039 fatcat:bzytghbvlredpdwqhd7fmrp3zm

Visual Re-Ranking via Adaptive Collaborative Hypergraph Learning for Image Retrieval [chapter]

Noura Bouhlel, Ghada Feki, Chokri Ben Amar
2020 Lecture Notes in Computer Science  
The potential of the hypergraph learning is essentially determined by the hypergraph construction scheme.  ...  Hypergraph has been widely used for relevance estimation, where textual results are taken as vertices and the re-ranking problem is formulated as a transductive learning on the hypergraph.  ...  To tackle those issues, recent works have proposed to leverage the regularized regression models, namely the sparse representation and the ridge regression for hypergraph construction [22] .  ... 
doi:10.1007/978-3-030-45439-5_34 fatcat:k2ince5j4zdkvo2f73zhkzmney

Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image

Jinhuan Xu, Liang Xiao, Jingxiang Yang
2021 Remote Sensing  
The unified model jointly learns the hypergraph and the discrete clustering labels, in which the subspace feature is adaptively learned by considering the optimal dynamic hypergraph with the self-taught  ...  Existing hypergraph learning methods only construct the hypergraph by a fixed similarity matrix or are adaptively optimal in original feature space; they do not update the hypergraph in subspace-dimensionality  ...  [30, 31] proposed an unsupervised sparse feature selection method by embedding a hypergraph Laplacian regularizer, in which the hypergraph was learned dynamically from the optimized sparse subspace  ... 
doi:10.3390/rs13071372 fatcat:fn3o7hqiyvfhdbo6c7tv7iaks4

Regression-based Hypergraph Learning for Image Clustering and Classification [article]

Sheng Huang and Dan Yang and Bo Liu and Xiaohong Zhang
2016 arXiv   pre-print
Inspired by the recently remarkable successes of Sparse Representation (SR), Collaborative Representation (CR) and sparse graph, we present a novel hypergraph model named Regression-based Hypergraph (RH  ...  Moreover, we plug RH into two conventional hypergraph learning frameworks, namely hypergraph spectral clustering and hypergraph transduction, to present Regression-based Hypergraph Spectral Clustering  ...  For examples, RH can be further applied to hypergraph-based subspace learning [8] , feature selection [56] , multi-label learning [6] , [57] or attribute learning [11] .  ... 
arXiv:1603.04150v1 fatcat:dq2ql3qnhfblvi6rvh5nwuilzi

Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification

Qingshan Liu, Yubao Sun, Cantian Wang, Tongliang Liu, Dacheng Tao
2017 IEEE Transactions on Image Processing  
To simultaneously cope with these drawbacks, we propose a new elastic net hypergraph learning model, which consists of two steps.  ...  New hypergraph learning algorithms, including unsupervised clustering and multi-class semi-supervised classification, are then derived.  ...  The hypergraph semi-supervised learning model can be formulated as the following regularization problem, arg min F R emp (F ) + λΩ(F ), (13) where Ω(F ) is a regularizer on the hypergraph, R emp (F ) is  ... 
doi:10.1109/tip.2016.2621671 pmid:28113763 fatcat:kh2oo37vlbfv5h2hcx5igczhpq

Hypergraph Label Propagation Network

Yubo Zhang, Nan Wang, Yufeng Chen, Changqing Zou, Hai Wan, Xinbin Zhao, Yue Gao
hypergraph learning through an end-to-end architecture.  ...  In this paper, we propose a Hypergraph Label Propagation Network (HLPN) which combines hypergraph-based label propagation and deep neural networks in order to optimize the feature embedding for optimal  ...  In the framework, λ is the trade-off parameter to balance the influence of the hypergraph structure regularizer Ω(F) and the empirical loss R emp (F). F is the to-be-learned label matrix.  ... 
doi:10.1609/aaai.v34i04.6170 fatcat:pupx2zi6nnbkdlofzw6lj3252e

Hypergraph $p$ -Laplacian Regularization for Remotely Sensed Image Recognition

Xueqi Ma, Weifeng Liu, Shuying Li, Dapeng Tao, Yicong Zhou
2019 IEEE Transactions on Geoscience and Remote Sensing  
Graph based SSL and manifold regularization based SSL including Laplacian regularization (LapR) and Hypergraph Laplacian regularization (HLapR) are representative SSL methods and have achieved prominent  ...  In this paper, we present an effect and effective approximation algorithm of Hypergraph p-Laplacian and then propose Hypergraph p-Laplacian regularization (HpLapR) to preserve the geometry of the probability  ...  [17] built a model of sparse feature selection-based manifold regularization (SFSMR) to select the optimal information and preserve the underlying manifold structure of data for scene recognition.  ... 
doi:10.1109/tgrs.2018.2867570 fatcat:ai5fo3jrrndf7hs7by2gx4dfou
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