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A Neural Network for Semi-supervised Learning on Manifolds [chapter]

Alexander Genkin, Anirvan M. Sengupta, Dmitri Chklovskii
2019 Lecture Notes in Computer Science  
Semi-supervised learning algorithms typically construct a weighted graph of data points to represent a manifold.  ...  Here, we propose a feed-forward neural network capable of semi-supervised learning on manifolds without using an explicit graph representation.  ...  Acknowledgements The authors are grateful to Victor Minden and Mariano Tepper for their insightful comments. We thank Johannes Friedrich, Tiberiu Tesileanu and Charles Windolf for helpful discussions.  ... 
doi:10.1007/978-3-030-30487-4_30 fatcat:yl36envzvvfhzdngy6drey6dva

Generative Manifold Learning for the Exploration of Partially Labeled Data

Raúl Cruz-Barbosa, Alfredo Vellido
2013 Journal of Computacion y Sistemas  
We then proceed to define a novel semi-supervised model, SS-Geo-GTM, that extends Geo-GTM to deal with semi-supervised problems.  ...  The resulting proximity graph is used as the basis for a class label propagation algorithm.  ...  Cruz-Barbosa acknowledges the Mexican Secretariat of Public Education (SEP-PROMEP program) for his PhD grant.  ... 
doi:10.13053/cys-17-4-2013-014 fatcat:bxvoxaczqnan7mop6wx7sojqwy

Annotating personal albums via web mining

Jimin Jia, Nenghai Yu, Xian-Sheng Hua
2008 Proceeding of the 16th ACM international conference on Multimedia - MM '08  
A multi-graph similarity propagation based semisupervised learning (MGSP-SSL) algorithm is used to suppress the noises in the initial annotations from the Web.  ...  An effective automatic annotation system for personal albums is desired for both efficient browsing and search.  ...  ACKNOWLEDGEMENTS We would like to thank the reviewers for their valuable and constructive comments.  ... 
doi:10.1145/1459359.1459421 dblp:conf/mm/JiaYH08 fatcat:rbmqunfgrjbevdgfvypkt3fclm

Visual Understanding via Multi-Feature Shared Learning With Global Consistency

Lei Zhang, David Zhang
2016 IEEE transactions on multimedia  
is much improved through the semi-supervised learning with global label consistency.  ...  Additionally, a group graph manifold regularizer composed of the Laplacian and Hessian graph is proposed for better preserving the manifold structure of each feature, such that the label prediction power  ...  Second, motivated by the semi-supervised manifold regression, a group graph manifold regularizer composed of the weighted Laplacian and Hessian graphs of multiple features is proposed for manifold structure  ... 
doi:10.1109/tmm.2015.2510509 fatcat:cp4rtxaha5dblgrg6sugmsa7sa

Semi-Supervised Multiple Feature Analysis for Action Recognition

Sen Wang, Zhigang Ma, Yi Yang, Xue Li, Chaoyi Pang, Alexander G. Hauptmann
2014 IEEE transactions on multimedia  
To deal with this problem, a graph is utilized to approximate the density and manifold information for semi-supervised learning in the framework.  ...  The main paradigm of graph-based semi-supervised learning is to utilize relations between labeled and unlabeled data by exploring the manifold structure.  ...  He mainly focuses his research on machine learning and relevant applications in computer vision and data mining, e.g., human action recognition, social network event detection, etc. Zhigang  ... 
doi:10.1109/tmm.2013.2293060 fatcat:caiu5if4trf73fmpqs5jdbjudi

Bidirectional Semi-supervised Learning with Graphs [chapter]

Tomoharu Iwata, Kevin Duh
2012 Lecture Notes in Computer Science  
We present a machine learning task, which we call bidirectional semi-supervised learning, where label-only samples are given as well as labeled and unlabeled samples.  ...  Then, we propose a simple and effective graph-based method for bidirectional semisupervised learning in multi-label classification.  ...  Semi-supervised learning methods uses this information for improving performance.  ... 
doi:10.1007/978-3-642-33486-3_19 fatcat:txhwjpj3f5b2bgcelirxuacfqq

Discriminating Joint Feature Analysis for Multimedia Data Understanding

Zhigang Ma, Feiping Nie, Yi Yang, Jasper R. R. Uijlings, Nicu Sebe, Alexander G. Hauptmann
2012 IEEE transactions on multimedia  
It is a widely used technique to extend many algorithms to semi-supervised scenarios for its capability of leveraging the manifold structure of multimedia data.  ...  This feature selection approach was shown to be robust and efficient in literature as it considers the correlation between different features jointly when conducting feature selection; (2) manifold learning  ...  Finally, the graph Laplacian L is constructed through L = D − G. The graph Laplacian is the basis of semi-supervised learning.  ... 
doi:10.1109/tmm.2012.2199293 fatcat:ryganeudhfek3k4apfyiacb5ti

Semi-supervised clinical text classification with Laplacian SVMs: An application to cancer case management

Vijay Garla, Caroline Taylor, Cynthia Brandt
2013 Journal of Biomedical Informatics  
Semi-supervised learning algorithms use both labeled and unlabeled data to train classifiers, and can outperform their supervised counterparts.  ...  Performance improved with the number of labeled and unlabeled notes used to train the Laplacian SVM (pearson's ρ=0.529 for correlation between number of unlabeled notes and macro-F1 score).  ...  (NCATS), and VA Grant HIR 08-374 HSR&D: Consortium for Health Informatics.  ... 
doi:10.1016/j.jbi.2013.06.014 pmid:23845911 pmcid:PMC3806632 fatcat:ntcn3kqcpjftfinqdu66hqi6g4

A survey on semi-supervised learning

Jesper E. van Engelen, Holger H. Hoos
2019 Machine Learning  
We focus primarily on semi-supervised classification, where the large majority of semi-supervised learning research takes place.  ...  Semi-supervised learning is the branch of machine learning concerned with using labelled as well as unlabelled data to perform certain learning tasks.  ...  Acknowledgements We thank Matthijs van Leeuwen for his valuable feedback on drafts of this article.  ... 
doi:10.1007/s10994-019-05855-6 fatcat:lm2obxiqtrcujfbyzz3erna5p4

Kernel selection forl semi-supervised kernel machines

Guang Dai, Dit-Yan Yeung
2007 Proceedings of the 24th international conference on Machine learning - ICML '07  
Existing semi-supervised learning methods are mostly based on either the cluster assumption or the manifold assumption.  ...  When the manifold assumption is incorporated, graph Laplacian kernels are used as the basic kernels for learning an optimal convex combination of graph Laplacian kernels.  ...  For the convenience of referencing, KS-SL (kernel selection for supervised learning) refers to the method in (Argyriou et al., 2005) and GLKS-SSL (graph Laplacian kernel selection for semi-supervised  ... 
doi:10.1145/1273496.1273520 dblp:conf/icml/DaiY07 fatcat:trwhg5n6krcw3aqiamwvs56zuy

Hyperspectral Image Classification by Using Pixel Spatial Correlation [chapter]

Yue Gao, Tat-Seng Chua
2013 Lecture Notes in Computer Science  
Semi-supervised learning on the constructed hypergraph is conducted for hyperspectral image classification. Experiments on two datasets are used to evaluate the performance of the proposed method.  ...  To better employ the spatial information, we propose to estimate the correlation among pixels in a hypergraph structure.  ...  A semi-supervised graph-based learning method [4] is introduced to represent the hyperspectral image by using a graph structure, and then a semi-supervised learning process on the graph is conducted  ... 
doi:10.1007/978-3-642-35725-1_13 fatcat:poya3tmuxnhbxnbnrjyyqwer6q

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
for semi-supervised learning tasks.  ...  such as the Low-Rank Representation (LRR), and propose a novel semi-supervised graph learning method called Semi-Supervised Low-Rank Representation (SSLRR).  ...  SEMI-SUPERVISED GRAPH LEARNING In this section, we use the LRR-graph [4] as a representative example to describe our semi-supervised graph learning framework.  ... 
arXiv:1607.02539v3 fatcat:va6eqworsfeidagiarl4nyouhi

Exploring Urban Dynamics Based on Pervasive Sensing: Correlation Analysis of Traffic Density and Air Quality

Wenzhu Zhang, Bing Zhu, Lin Zhang, Jian Yuan, Ilsun You
2012 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing  
Second, we design a Spatial-Temporal Manifold Learning (STML) algorithm to analyse the correlation between physical processes.  ...  The results show great potential of STML for future urban sensing applications.  ...  That is standard semi-supervised learning problem, as we discussed above. Manifold learning method based on spectral graph theory is proposed to solve this problem.  ... 
doi:10.1109/imis.2012.137 dblp:conf/imis/ZhangZZYY12 fatcat:4zhpcshrtndqdnd6laj3x4337a

Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised Learning [article]

Bo Jiang, Doudou Lin
2018 arXiv   pre-print
Recently, graph convolutional network (GCN) has been widely used for semi-supervised classification and deep feature representation on graph-structured data.  ...  To overcome this limitation, we introduce a graph Laplacian GCN (gLGCN) approach for graph data representation and semi-supervised classification.  ...  Graph Laplacian regularization One kind of popular method for semi-supervised learning problem is to use graph-based semisupervised learning, where the label information is smoothed over the graph via  ... 
arXiv:1809.09839v1 fatcat:vemtdodqcffw3d6uzj47maa244

Pattern Based Network Security Using Semi-supervised Learning

Vinod K Pachghare, Vaibhav K Khatavkar, Parag A Kulkarni
2012 International Journal of Information and Network Security (IJINS)  
The machine learning techniques used for solving intrusion detection problem can be broadly classified into three broad categories: Unsupervised, supervised and semi-supervised.  ...  So in this work we propose a semi-supervised approach for pattern based IDS to improve performance of supervised approach. The experimentation is performed on KDD CUP99 dataset.  ...  Chien-Yi Chiu et al proposed Semi-supervised Learning for False Alarm Reduction.  ... 
doi:10.11591/ijins.v1i3.704 fatcat:vfl3kxm2dzbjjdbjwucgpy5hmq
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