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Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises

Ehsan Adeli, Kim-Han Thung, Le An, Guorong Wu, Feng Shi, Tao Wang, Dinggang Shen
2018 IEEE Transactions on Pattern Analysis and Machine Intelligence  
In this paper, we propose a semi-supervised robust discriminative classification method based on the least-squares formulation of linear discriminant analysis to detect sample-outliers and feature-noises  ...  ., medical image analysis problems), in which there often exist outlier data points (sample-outliers) and noises in the predictor values (feature-noises).  ...  Conclusion In this paper, we proposed a novel approach for discriminative classification, which is robust against both sample-outliers and feature-noises.  ... 
doi:10.1109/tpami.2018.2794470 pmid:29994560 pmcid:PMC6050136 fatcat:vcztnlvzgfatta2uzikafhn2ii

Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis

Ehsan Adeli-Mosabbeb, Kim-Han Thung, Le An, Feng Shi, Dinggang Shen
2015 Neural Information Processing Systems  
Robust discriminative models are somewhat scarce and only a few attempts have been made to make them robust against noise or outliers.  ...  In this paper, we propose a classification method based on the least-squares formulation of linear discriminant analysis, which simultaneously detects the sample-outliers and feature-noises.  ...  To this end, we introduce a semi-supervised discriminative classification model, which, unlike previous works, jointly estimates the noise model (both sample-outliers and feature-noises) on the whole labeled  ... 
dblp:conf/nips/Adeli-MosabbebT15 fatcat:rnt2gaofxnddtdzppzgh35jvry

Robust Semi-Supervised Classification using GANs with Self-Organizing Maps [article]

Ronald Fick, Paul Gader, Alina Zare
2021 arXiv   pre-print
Generative adversarial networks (GANs) have shown tremendous promise in learning to generate data and effective at aiding semi-supervised classification.  ...  The problem of discriminating outliers from inliers while maintaining classification accuracy is referred to here as the DOIC problem.  ...  The combination of spectral and SOM features was demonstrated to not lose any classification performance vs pure spectral features, but gain significant performance in terms of robustness vs outliers  ... 
arXiv:2110.10286v1 fatcat:tpbwc2lxqzea3ntklwvxp3myou

Semi-Supervised Outlier Detection Using A Generative And Adversary Framework

Jindong Gu, Matthias Schubert, Volker Tresp
2018 Zenodo  
In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated  ...  The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier.  ...  The second section presents the work related to one-class classification problems (i.e., semi-supervised outlier detection).  ... 
doi:10.5281/zenodo.1474926 fatcat:oqgillenrngsvfcl73nuni3ivu

Adaptive Subspaces for Few-Shot Learning

Christian Simon, Piotr Koniusz, Richard Nock, Mehrtash Harandi
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
We will empirically show that such modelling leads to robustness against perturbations (e.g., outliers) and yields competitive results on the task of supervised and semi-supervised few-shot classification  ...  Generalization from limited samples, usually studied under the umbrella of meta-learning, equips learning techniques with the ability to adapt quickly in dynamical environments and proves to be an essential  ...  We showed that DSN is robust to noise in few-shot learning.  ... 
doi:10.1109/cvpr42600.2020.00419 dblp:conf/cvpr/SimonKNH20 fatcat:oysynu2m3je6rgkk3m5ixbu5ri

A robust approach to model-based classification based on trimming and constraints [article]

Andrea Cappozzo, Francesca Greselin, Thomas Brendan Murphy
2019 arXiv   pre-print
The present work introduces a robust modification to the Model-Based Classification framework, employing impartial trimming and constraints on the ratio between the maximum and the minimum eigenvalue of  ...  The proposed method effectively handles noise presence in both response and exploratory variables, providing reliable classification even when dealing with contaminated datasets.  ...  Acknowledgements The authors are very grateful to Agustin Mayo-Iscar and Luis Angel García Escudero for both stimulating discussion and advices on how to enforce the eigenvalue-ratio constraints under  ... 
arXiv:1904.06136v1 fatcat:zhley2grgbgm7dlol7fzmrh56u

Possibilistic Clustering-Promoting Semi-Supervised Learning for EEG-Based Emotion Recognition

Yufang Dan, Jianwen Tao, Jianjing Fu, Di Zhou
2021 Frontiers in Neuroscience  
information, which improves the robustness of the method to noise and the outlier.  ...  However, the existing GSSL methods are sensitive or not robust enough to noise or outlier electroencephalogram (EEG)-based data since each individual subject may present noise or outlier EEG patterns in  ...  AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.  ... 
doi:10.3389/fnins.2021.690044 doaj:67934632c343419bb8a1b9f18c0c5d84 fatcat:sggchpx7sfctfehhujtsngxkbm

Joint Semi-Supervised Feature Selection and Classification through Bayesian Approach

Bingbing Jiang, Xingyu Wu, Kui Yu, Huanhuan Chen
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In this paper, we propose a joint semi-supervised feature selection and classification algorithm (JSFS) which adopts a Bayesian approach to automatically select the relevant features and simultaneously  ...  Semi-supervised feature selection focuses on the problem of how to learn a relevant feature subset in the case of abundant unlabeled data with few labeled data.  ...  feature selection (CSFS) (Chang et al. 2014) , two filter-based semi-supervised feature selection algorithm: locality sensitive discriminant feature (LSDF) (Zhao, Lu, and He 2008) and semi-supervised  ... 
doi:10.1609/aaai.v33i01.33013983 fatcat:73qkvtwpnnawlfgfzf4jrqdfwe

Classification-Reconstruction Learning for Open-Set Recognition [article]

Ryota Yoshihashi, Wen Shao, Rei Kawakami, Shaodi You, Makoto Iida, Takeshi Naemura
2019 arXiv   pre-print
Extensive experiments reveal that the proposed method outperforms existing deep open-set classifiers in multiple standard datasets and is robust to diverse outliers.  ...  Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes  ...  Acknowledgement This work is in part supported by JSPS KAKENHI Grant Number JP18K11348, and Grant-in-Aid for JSPS Fellows JP16J04552. The authors would like to thank Dr.  ... 
arXiv:1812.04246v3 fatcat:obzhksdcmzc4fhmdoddodpqeim

Semi-supervised Robust Dictionary Learning via Efficient l-Norms Minimization

Hua Wang, Feiping Nie, Weidong Cai, Heng Huang
2013 2013 IEEE International Conference on Computer Vision  
However, traditional dictionary learning methods suffer from three weaknesses: sensitivity to noisy and outlier samples, difficulty to determine the optimal dictionary size, and incapability to incorporate  ...  In this paper, we address these weaknesses by learning a Semi-Supervised Robust Dictionary (SSR-D).  ...  Robustness against outlier samples.  ... 
doi:10.1109/iccv.2013.146 dblp:conf/iccv/WangNCH13 fatcat:o4bgsqvxrracxexx7gxcgbs5su

Semi-Supervised Training for Positioning of Welding Seams

Wenbin Zhang, Jochen Lang
2021 Sensors  
In this paper, we employ semi-supervised learning to simultaneously increase accuracy and robustness while avoiding expensive and time-consuming labeling efforts by a domain expert.  ...  While semi-supervised learning approaches for various image classification tasks exist, we purpose a novel algorithm for semi-supervised key-point detection for seam placement by a welding robot.  ...  As each discriminator has its own strategy to extract different types of features, it has the potential to capture different outliers.  ... 
doi:10.3390/s21217309 pmid:34770616 pmcid:PMC8588534 fatcat:2kbf7szvmzdsrmvj3zosd7vbbe

Subspace structural constraint-based discriminative feature learning via nonnegative low rank representation

Ao Li, Xin Liu, Yanbing Wang, Deyun Chen, Kezheng Lin, Guanglu Sun, Hailong Jiang, Kim Han Thung
2019 PLoS ONE  
To achieve extra discrimination, linear regression is also incorporated into our model to enforce the projection features around and close to their label-based centers.  ...  Feature subspace learning plays a significant role in pattern recognition, and many efforts have been made to generate increasingly discriminative learning models.  ...  Acknowledgments The authors are grateful to the editor and anonymous reviewers for their valuable review comments on our work.  ... 
doi:10.1371/journal.pone.0215450 pmid:31063497 pmcid:PMC6504107 fatcat:jkmjgu5j5najnpvh6777je3ukm

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  
Secondly, the -norm is applied to make the framework robust for noises and outliers.  ...  action recognition with a small impact from noises and outliers have been largely ignored so far.  ...  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

Probabilistic Fisher discriminant analysis: A robust and flexible alternative to Fisher discriminant analysis

Charles Bouveyron, Camille Brunet
2012 Neurocomputing  
This allows the proposed method to be robust to label noise and to be used in the semi-supervised context.  ...  Fisher discriminant analysis (FDA) is a popular and powerful method for dimensionality reduction and classification.  ...  This allows PFDA to be robust to label noise and to be used in the semi-supervised context.  ... 
doi:10.1016/j.neucom.2011.11.027 fatcat:2abwbbvxcvfkngxdr42sovzmzi

Semi-supervised Assessment of Incomplete LV Coverage in Cardiac MRI Using Generative Adversarial Nets [chapter]

Le Zhang, Ali Gooya, Alejandro F. Frangi
2017 Lecture Notes in Computer Science  
In this paper, we propose a novel semi-supervised method to check the coverage of LV from CMR images by using generative adversarial networks (GAN), we call it Semi-Coupled-GANs (SCGANs).  ...  First, the SCGANs generate adversarial examples and extract high-level features from the CMR images; then these image attributes are used to detect missing basal and apical slices.  ...  Performance and Discussion: We evaluate the quality of our semi-supervised representation learning algorithms by applying it as a feature extractor on supervised datasets.  ... 
doi:10.1007/978-3-319-68127-6_7 fatcat:r5elnhkcxfaz5ao6ui2y4y6my4
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