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Semi-Supervised Classification Based on Low Rank Representation

Xuan Hou, Guangjun Yao, Jun Wang
2016 Algorithms  
In this paper, we take advantage of low-rank representation for graph construction and propose an inductive semi-supervised classifier called Semi-Supervised Classification based on Low-Rank Representation  ...  Graph-based semi-supervised classification uses a graph to capture the relationship between samples and exploits label propagation techniques on the graph to predict the labels of unlabeled samples.  ...  Semi-Supervised Classification based on Low-Rank Representation (SSC-LRR).  ... 
doi:10.3390/a9030048 fatcat:lyxvww5tw5fh7mzz543faem6ne

Classification of Hyperspectral Images with Robust Regularized Block Low-Rank Discriminant Analysis

Baokai Zu, Kewen Xia, Wei Du, Yafang Li, Ahmad Ali, Sagnik Chakraborty
2018 Remote Sensing  
Due to the symmetric matrix requirements for the regularized graph of discriminant analysis, the k-nearest neighbor is applied to handle the whole low-rank graph integrally.  ...  Even with simple supervised and semi-supervised classifiers (nearest neighbor and SVM) and randomly given parameters, the feature extraction method achieves significant results with few labeled samples  ...  Table 2 .Table 3 . 23 Supervised and semi-supervised classification results for the Indian Pines image. BLRDA, Block Low-Rank Discriminant Analysis.  ... 
doi:10.3390/rs10060817 fatcat:x2hfjmmhcndshl234nsufaf6lm

Graph Based Semi-Supervised Learning via Structure Preserving Low-Rank Representation

Yong Peng, Xianzhong Long, Bao-Liang Lu
2014 Neural Processing Letters  
In this paper, we focus on the semi-supervised learning methods developed on data graph whose edge weights are measured by low-rank representation (LRR) coefficients.  ...  Semi-supervised learning works on utilizing both labeled and unlabeled data to improve learning performance, which has been receiving increasing attention in many applications such as clustering and classification  ...  We present some preliminaries on low-rank representation, augmented Lagrange multipliers method and semi-supervised classification framework in Sect. 2.  ... 
doi:10.1007/s11063-014-9396-z fatcat:htzcgb7jtva5fgizshvptctn2e

Structure Preserving Low-Rank Representation for Semi-supervised Face Recognition [chapter]

Yong Peng, Suhang Wang, Shen Wang, Bao-Liang Lu
2013 Lecture Notes in Computer Science  
Among these graph construction methods, low-rank representation based graph, which calculates the edge weights of both labeled and unlabeled samples as the low-rank representation (LRR) coefficients, has  ...  Constructing an informative and discriminative graph plays an important role in the graph based semi-supervised learning methods.  ...  Differing from the sparse representation which enforces the representation coefficients to be sparse, the semi-supervised low-rank representation graph (LRR) was proposed for pattern classification [12  ... 
doi:10.1007/978-3-642-42042-9_19 fatcat:zfp3k66a2rge7dpcri5yyzb6my

Graph Construction with Label Information for Semi-Supervised Learning [article]

Liansheng Zhuang, Zihan Zhou, Jingwen Yin, Shenghua Gao, Zhouchen Lin, Yi Ma, Nenghai Yu
2017 arXiv   pre-print
such as the Low-Rank Representation (LRR), and propose a novel semi-supervised graph learning method called Semi-Supervised Low-Rank Representation (SSLRR).  ...  for semi-supervised learning tasks.  ...  Semi-Supervised Low-Rank Representation In this subsection, we incorporate the label information of observed samples into the original LRR framework, and propose a new model called Semi-Supervised Low-Rank  ... 
arXiv:1607.02539v3 fatcat:va6eqworsfeidagiarl4nyouhi

Constructing a Nonnegative Low-Rank and Sparse Graph With Data-Adaptive Features

Liansheng Zhuang, Shenghua Gao, Jinhui Tang, Jingjing Wang, Zhouchen Lin, Yi Ma, Nenghai Yu
2015 IEEE Transactions on Image Processing  
Index Terms-Graph Construction, low-rank and sparse representation, semi-supervised learning, data embedding.  ...  First, we propose to build a nonnegative low-rank and sparse (referred to as NNLRS) graph for the given data representation.  ...  [21] proposed a new semi-supervised kernel low-rank presentation graph (SKLRG) by combining a low-rank representation with kernel trick.  ... 
doi:10.1109/tip.2015.2441632 pmid:26057712 fatcat:jhe7svay5zgpnf4bqgssh7xnq4

Sparse semi-supervised learning on low-rank kernel

Kai Zhang, Qiaojun Wang, Liang Lan, Yu Sun, Ivan Marsic
2014 Neurocomputing  
In this paper, we introduce L 1 -norm penalization on the low-rank factorized kernel for efficient, globally optimal model selection in graph-based semi-supervised learning.  ...  Of particular interest is the semi-supervised learning, where very few training samples are available among large volumes of unlabeled data.  ...  Inspired by it, we apply the L 1 -norm penalization on the expansion coefficients of the low-rank factorized kernel in graph-based semi-supervised learning.  ... 
doi:10.1016/j.neucom.2013.09.033 fatcat:2ukch2jzh5eupa6fmgm2pn6b54

Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning

Yong Peng, Bao-Liang Lu, Suhang Wang
2015 Neural Networks  
Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL).  ...  In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation.  ...  Motivated by the recent progress on LRR and manifold learning, we propose a novel manifold low-rank representation model to build graph for semi-supervised classification.  ... 
doi:10.1016/j.neunet.2015.01.001 pmid:25634552 fatcat:xmy7g3owfza6vnw63vafnbomvu

Matrix Completion for Graph-Based Deep Semi-Supervised Learning

Fariborz Taherkhani, Hadi Kazemi, Nasser M. Nasrabadi
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we introduce a new iterative Graph-based Semi-Supervised Learning (GSSL) method to train a CNN-based classifier using a large amount of unlabeled data and a small amount of labeled data.  ...  In this graph, the missing label of unsupervised nodes is predicted by using a matrix completion method based on rank minimization criterion.  ...  Experimental results show that our model is comparable to the state of the art for Semi-Supervised image classification.  ... 
doi:10.1609/aaai.v33i01.33015058 fatcat:qc4pxvkto5gu7n5pe5ofo53qnq

A New Graph Constructor for Semi-supervised Discriminant Analysis via Group Sparsity

Haoyuan Gao, Liansheng Zhuang, Nenghai Yu
2011 2011 Sixth International Conference on Image and Graphics  
Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly labeled data.  ...  This paper studies the Semi-supervised Discriminant Analysis (SDA) algorithm, which aims at dimensionality reduction utilizing both limited labeled data and abundant unlabeled data.  ...  The k-NN graph is more robust than it. 3) Lle-graph is also stable for the classification, but the performance is not as good as 1 graph and our method. 4) For the low-labeled rate semi-supervised rejection  ... 
doi:10.1109/icig.2011.82 dblp:conf/icig/GaoZY11 fatcat:ju6j36znsnggzm7kv2iguyxjlu

Semi-supervised Eigenvectors for Large-scale Locally-biased Learning [article]

Toke J. Hansen, Michael W. Mahoney
2013 arXiv   pre-print
in a semi-supervised manner.  ...  In this paper, we address this issue by providing a methodology to construct semi-supervised eigenvectors of a graph Laplacian, and we illustrate how these locally-biased eigenvectors can be used to perform  ...  , which performs transductive learning via spectral graph partitioning.  ... 
arXiv:1304.7528v1 fatcat:riosluvi2zh3de7qts2yw23dvy

SLIC Superpixel-Based l2,1-Norm Robust Principal Component Analysis for Hyperspectral Image Classification

Baokai Zu, Kewen Xia, Tiejun Li, Ziping He, Yafang Li, Jingzhong Hou, Wei Du
2019 Sensors  
Finally, a semi-supervised dimensionality reduction framework based on SURPCA2,1 graph is used for feature extraction task.  ...  Analysis (SURPCA2,1), which integrates superpixel segmentation method Simple Linear Iterative Clustering (SLIC) into Low-rank Decomposition.  ...  The k-Nearest Neighbor (kNN) algorithm is applied to handle the low-rank graph for the regularized graph of semi-supervised discriminant analysis.  ... 
doi:10.3390/s19030479 fatcat:75e3nq3nprgobo2rbwuepqqfaa

Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features [article]

Liansheng Zhuang, Shenghua Gao, Jinhui Tang, Jingjing Wang, Zhouchen Lin, Yi Ma
2014 arXiv   pre-print
Extensive experiments on three publicly available datasets demonstrate that the proposed method outperforms the state-of-the-art graph construction method by a large margin for both semi-supervised classification  ...  This paper aims at constructing a good graph for discovering intrinsic data structures in a semi-supervised learning setting.  ...  CONCLUSION This paper proposes a novel informative graph, called the nonnegative low rank and sparse graph (NNLRS-graph), for graph-based semi-supervised learning.  ... 
arXiv:1409.0964v1 fatcat:rxgmo3denbfyvfmbjtxinwyzdu

Semi-supervised learning for ordinal Kernel Discriminant Analysis

M. Pérez-Ortiz, P.A. Gutiérrez, M. Carbonero-Ruz, C. Hervás-Martínez
2016 Neural Networks  
Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters  ...  Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised  ...  learning in the context of semi-supervised ordinal classification.  ... 
doi:10.1016/j.neunet.2016.08.004 pmid:27639724 fatcat:d3uwjsrdwzfftdo7mgr57vw7ey

Non-negative low rank and sparse graph for semi-supervised learning

Liansheng Zhuang, Haoyuan Gao, Zhouchen Lin, Yi Ma, Xin Zhang, Nenghai Yu
2012 2012 IEEE Conference on Computer Vision and Pattern Recognition  
We demonstrate the effectiveness of NNLRS-graph in semi-supervised classification and discriminative analysis.  ...  This paper proposes a novel non-negative low-rank and sparse (NNLRS) graph for semisupervised learning.  ...  Conclusion This paper proposes a novel informative graph, called the nonnegative low rank and sparse graph (NNLRS-graph), for graph-based semi-supervised learning.  ... 
doi:10.1109/cvpr.2012.6247944 dblp:conf/cvpr/ZhuangGLMZY12 fatcat:4c6ibn6ijnbs7ga3giivxpl7fi
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