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A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and k-Nearest Neighbor Graph

Baokai Zu, Kewen Xia, Yongke Pan, Wenjia Niu
2017 Computational Intelligence and Neuroscience  
In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph.  ...  Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods.  ...  F2013202102), and Hebei Province Foundation for Returned Scholars (no. C2012003038).  ... 
doi:10.1155/2017/9290230 pmid:28316616 pmcid:PMC5338073 fatcat:3vqg2fiz4zfsxfxuokqjc7owgi

Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression

Feiping Nie, Dong Xu, Xuelong Li, Shiming Xiang
2011 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
These virtual labels are thereafter regressed with a linear model to calculate the projection matrix for dimensionality reduction.  ...  Semisupervised dimensionality reduction has been attracting much attention as it not only utilizes both labeled and unlabeled data simultaneously, but also works well in the situation of out-of-sample.  ...  This paper proposes a novel approach for semisupervised dimensionality reduction.  ... 
doi:10.1109/tsmcb.2010.2085433 pmid:21118781 fatcat:xkmhrofetve2jnvlp5rx4w3424

Embedding Tangent Space Extreme Learning Machine for EEG Decoding in Brain Computer Interface Systems

Mingwei Zhang, Yao Hou, Rongnian Tang, Youjun Li, Radek Matušů
2021 Journal of Control Science and Engineering  
This approach enables a low-dimensional embedding to capture more discriminant information for classification by efficiently incorporating information from testing and training data into a Riemannian graph  ...  To improve these problems and enhance the performance of the BCI system, in this study, we propose a novel semisupervised locality-preserving graph embedding model to learn a low-dimensional embedding.  ...  because the EEG covariance features endowed with a Riemannian metric formed a Riemannian manifold. (2) A novel framework of graph embedding is proposed for the constructed Riemannian graph. e calculation  ... 
doi:10.1155/2021/9959195 fatcat:sor543wkdjep3alxo2ajgv6b5i

Semisupervised Kernel Marginal Fisher Analysis for Face Recognition

Ziqiang Wang, Xia Sun, Lijun Sun, Yuchun Huang
2013 The Scientific World Journal  
To effectively cope with this problem, a novel dimensionality reduction algorithm called semisupervised kernel marginal Fisher analysis (SKMFA) for face recognition is proposed in this paper.  ...  Dimensionality reduction is a key problem in face recognition due to the high-dimensionality of face image.  ...  unlabeled high-dimensional data samples.  ... 
doi:10.1155/2013/981840 pmid:24163638 pmcid:PMC3791838 fatcat:fpynhn4ohnhtbcoqhu3frv45bu

Low-Rank Kernel-Based Semisupervised Discriminant Analysis

Baokai Zu, Kewen Xia, Shuidong Dai, Nelofar Aslam
2016 Applied Computational Intelligence and Soft Computing  
Semisupervised Discriminant Analysis (SDA) aims at dimensionality reduction with both limited labeled data and copious unlabeled data, but it may fail to discover the intrinsic geometry structure when  ...  Since LRR can capture the global data structures and get the lowest rank representation in a parameter-free way, the low-rank kernel method is extremely effective and robust for kinds of data.  ...  F2013202102), and Hebei Province Foundation for Returned Scholars (no. C2012003038).  ... 
doi:10.1155/2016/2783568 fatcat:npmxg74d5raydfp73ase7w4i4e

Table of Contents

2022 IEEE Transactions on Neural Networks and Learning Systems  
Yin 61 Semisupervised Classification With Novel Graph Construction for High-Dimensional Data ..............................  ...  Prescribed Performance Quantized Tracking Control for a Class of Delayed Switched Nonlinear Systems With Actuator Learning Robust Discriminant Subspace Based on Joint L 2,p -and L 2,s -Norm Distance Metrics  ... 
doi:10.1109/tnnls.2021.3134569 fatcat:i4njs3auvreefegwymv3uhvs7a

Semisupervised Discriminative Locally Enhanced Alignment for Hyperspectral Image Classification

Qian Shi, Liangpei Zhang, Bo Du
2013 IEEE Transactions on Geoscience and Remote Sensing  
Experiments with extensive hyperspectral image data sets showed that the proposed algorithm is notably superior to other state-of-the-art dimensionality reduction methods for hyperspectral image classification  ...  Furthermore, two key problems determining the performance of semisupervised methods are discussed in this paper.  ...  Gamba from the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society for providing the ROSIS data set and the handling editor and anonymous reviewers for their careful reading  ... 
doi:10.1109/tgrs.2012.2230445 fatcat:w437tg7jhngp7fm2pzup7bl6ue

A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization

Hongwei Ge, Zehang Yan, Jing Dou, Zhen Wang, ZhiQiang Wang
2018 Mathematical Problems in Engineering  
This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic image annotation with multilabel images.  ...  First, we construct a graph embedding term in the multiview NMF based on the association diagrams between labels for semantic constraints.  ...  For feature fusion and dimension deduction, a novel graph embedding term is constructed based on the relevant graph and the irrelevant graph.  ... 
doi:10.1155/2018/5987906 fatcat:ygbgib5adncubb3vsuwxvxubnu

Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing

Shuang Li, Bing Liu, Chen Zhang
2016 Computational Intelligence and Neuroscience  
But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning.  ...  To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method.  ...  Acknowledgments This work was supported by the National Natural Science Foundation of China (nos. 61403394 and 71573256) and the Fundamental Research Funds for the Central Universities (2014QNA46).  ... 
doi:10.1155/2016/4920670 pmid:27247562 pmcid:PMC4876218 fatcat:3j4okusej5cwnct7fgt4zbwmui

Semisupervised Deep Embedded Clustering with Adaptive Labels

Zhikui Chen, Chaojie Li, Jing Gao, Jianing Zhang, Peng Li, Boxiang Dong
2021 Scientific Programming  
To tackle this challenge, a semisupervised deep embedded clustering algorithm with adaptive labels is proposed to cluster those data in a semisupervised end-to-end manner on the basis of a little priori  ...  Then, to train parameters of the deep semisupervised clustering network, a back-propagation-based algorithm with adaptive labels is introduced based on the pretrain and fine-tune strategies.  ...  Recently, deep clustering has attracted much attention with the increasing collection of high-dimensional data.  ... 
doi:10.1155/2021/6613452 fatcat:wg7z2f3wgvgp3ksiduls5wrsxu

Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction

Wei Zhang, Zhouchen Lin, Xiaoou Tang
2011 IEEE Transactions on Knowledge and Data Engineering  
Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm for supervised feature extraction.  ...  In this paper, we propose a novel algorithm, called Semisupervised Semi-Riemannian Metric Map (S 3 RMM), following the geometric framework of semi-Riemannian manifolds.  ...  The first author would also like to thank Guangcan Liu et al. for sharing their manuscript on unsupervised semi-Riemannian metric map [17] and Deli Zhao for his valuable comments.  ... 
doi:10.1109/tkde.2010.143 fatcat:oycbi3jb7rahtnxuoapulvv3v4

A Novel Semi-supervised Classification Method Based on Class Certainty of Samples [chapter]

Fei Gao, Zhenyu Yue, Qingxu Xiong, Jun Wang, Erfu Yang, Amir Hussain
2018 Lecture Notes in Computer Science  
2018) Novel semi-supervised classification method based on class certainty of samples. Abstract.  ...  The results demonstrate that the proposed method can effectively exploit the information of unlabeled samples and greatly improve the classification effect compared with other state-of-the-art approaches  ...  As for SDA, it focuses on maintaining the neighborhood relationship between samples, but has a high requirement of data distribution.  ... 
doi:10.1007/978-3-030-00563-4_30 fatcat:lpo736j2hresbaf2sj4bgg2wbm

Noise-robust classification with hypergraph neural network

Nguyen Trinh Vu Dang, Loc Tran, Linh Tran
2021 Indonesian Journal of Electrical Engineering and Computer Science  
Then, the classic graph based semisupervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph  ...  and to reduce the runtime constructing the hypergraph of the hypergraph neural network method.  ...  Recently, to deal with irregular data structures, data scientists have gained many interests in graph convolution neural network method such as [3] .  ... 
doi:10.11591/ijeecs.v21.i3.pp1465-1473 fatcat:iz7g63by3vgofnrj6gocz5b4zy

Dark Web Data Classification Using Neural Network

Anand Singh Rajawat, Pradeep Bedi, S. B. Goyal, Sandeep Kautish, Zhang Xihua, Hanan Aljuaid, Ali Wagdy Mohamed, Ahmed Mostafa Khalil
2022 Computational Intelligence and Neuroscience  
Uncertain classification results cause a problem of not being able to predict user behavior. Since data of multidimensional nature has feature mixes, it has an adverse influence on classification.  ...  The data associated with Dark Web inundation has restricted us from giving the appropriate solution according to the need.  ...  high dimensional pictures.  ... 
doi:10.1155/2022/8393318 pmid:35387252 pmcid:PMC8979735 fatcat:agotv724mvapzp3btmy4alkpfu

Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

Eryang Chen, Ruichun Chang, Kaibo Shi, Ansheng Ye, Fang Miao, Jianghong Yuan, Ke Guo, Youhua Wei, Yiping Li, Zi-Peng Wang
2021 Discrete Dynamics in Nature and Society  
This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification.  ...  For all three real datasets, our method achieved competitive results with only 10 training samples.  ...  Conclusions and Further Research In this study, we developed a novel decision fusion method for HSI data classification.  ... 
doi:10.1155/2021/9998185 fatcat:bwrxa3xnizba5g2sy7q6kv24py
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