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Online Semi-supervised Multi-label Classification with Label Compression and Local Smooth Regression
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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In this paper, we present a novel Online Semi-supervised Multi-Label learning algorithm (OnSeML) based on label compression and local smooth regression, which allows real-time multi-label predictions in ...
Online semi-supervised multi-label classification serves a practical yet challenging task since only a small number of labeled instances are available in real streaming environments. ...
Fundamental Research Funds for the Central Universities (ZYGX2019Z014), Fok Ying-Tong Education Foundation for Young Teachers in the Higher Education Institutions of China (161062), National key research and ...
doi:10.24963/ijcai.2020/189
dblp:conf/ijcai/LiWBS20
fatcat:56nnzcspwrh4xefatldpzhdoti
Some new directions in graph-based semi-supervised learning
2009
2009 IEEE International Conference on Multimedia and Expo
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(ii) online semi-supervised learning that learns incrementally with low computation and memory needs; and (iii) learning spectrally sparse but non-smooth labels with compressive sensing. ...
is restricted to live on a single manifold; (2) learning must happen in batch mode; and (3) the target label is assumed smooth on the manifold. ...
And training must be cheap and quick. What we need is semi-supervised learning that operates in online mode. (3) Current methods assume label smoothness on the graph. ...
doi:10.1109/icme.2009.5202789
dblp:conf/icmcs/ZhuGK09
fatcat:2v5ss5iowfewhbb7w4a5ysiqti
The Emerging Trends of Multi-Label Learning
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2011.11197v1 fatcat:zexkygo5tzf4pi7p2ig45kjdeuOther Versions
data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. ...
For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extremely large number of classes or labels; utilizing massive ...
Semi-Supervised Multi-Label Classification In semi-supervised MLC (SS-MLC) [117] , the dataset is comprised of two sets: fully labeled data and unlabeled data. ...
arXiv:2011.11197v2
fatcat:hu6w4vgnwbcqrinrdfytmmjbjm
A Regularized Approach for Geodesic-Based Semisupervised Multimanifold Learning
2014
IEEE Transactions on Image Processing
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The method consists of three techniques: a semisupervised graph construction method, replacement of original data points with feature vectors which are built by geodesic distances, and a new semisupervised ...
However, most geodesic distance-based manifold learning algorithms have two limitations when applied to classification: 1) class information is rarely used in computing the geodesic distances between data ...
In order to better use the label information and manifold structure of data for semi-supervised learning, we replace the Semi-Supervised Discriminant Analysis method [17] to a more robust semi-supervised ...
doi:10.1109/tip.2014.2312643
pmid:24723575
fatcat:pzblshuvifetrdg5cerfldv6lm
Author Index
2010
2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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Workshop: Tracking of Cell Population from Time Lapse and End Point Confocal Microscopy
Images with Multiple Hypothesis Kalman Smoothing Filters
Orabona, Francesco
Online-Batch Strongly Convex Multi ...
Workshop: Automated Pose Estimation in 3D Point Clouds Applying Annealing Particle Filters and
Inverse Kinematics on a GPU
Leistner, Christian
Online Multi-Class LPBoost
On-line Semi-supervised Multiple-Instance ...
doi:10.1109/cvpr.2010.5539913
fatcat:y6m5knstrzfyfin6jzusc42p54
2020 Index IEEE/ACM Transactions on Audio, Speech, and Language Processing Vol. 28
2020
IEEE/ACM Transactions on Audio Speech and Language Processing
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., +, TASLP 2020 813-824 Semi-Supervised Neural Chord Estimation Based on a Variational Autoencoder With Latent Chord Labels and Features. ...
., +, TASLP 2020 592-604
Semi-Supervised Neural Chord Estimation Based on a Variational Auto-
encoder With Latent Chord Labels and Features. ...
T Target tracking Multi-Hypothesis Square-Root Cubature Kalman Particle Filter for Speaker Tracking in Noisy and Reverberant Environments. Zhang, Q., +, TASLP 2020 1183 -1197 ...
doi:10.1109/taslp.2021.3055391
fatcat:7vmstynfqvaprgz6qy3ekinkt4
Semi-supervised low-rank mapping learning for multi-label classification
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In this paper, we proposed a semi-supervised low-rank mapping (SLRM) model to handle these two challenges. ...
Multi-label problems arise in various domains including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. ...
Therefore, it is very important to develop semi-supervised multi-label learning methods that can use both multi-labeled data and unlabeled data together to deal with this important problem. ...
doi:10.1109/cvpr.2015.7298755
dblp:conf/cvpr/JingYYN15
fatcat:inf2fasqyjgqbd5pv2yozis4qm
Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval
2015
IEEE Transactions on Medical Imaging
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Specifically, we present a supervised kernel hashing technique which leverages a small amount of supervised information in learning to compress a 10 000-dimensional image feature vector into only tens ...
Extensive evaluations are carried out in terms of image classification (i.e., benign versus actionable categorization) and retrieval tests. ...
In other words, hash bits can simultaneously encode local textural features with semantic labels.
V. ...
doi:10.1109/tmi.2014.2361481
pmid:25314696
fatcat:frt22wzviraffm7zjex5wgzlau
Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection
[article]
2020
arXiv
pre-print
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Weakly Supervised Object Detection (WSOD) has emerged as an effective tool to train object detectors using only the image-level category labels. ...
However, without object-level labels, WSOD detectors are prone to detect bounding boxes on salient objects, clustered objects and discriminative object parts. ...
Acknowledgments and Disclosure of Funding ...
arXiv:2010.12023v1
fatcat:yg25zw3bjrb5ze4h4brbod7r7u
Domain Adaptation for Visual Applications: A Comprehensive Survey
[article]
2017
arXiv
pre-print
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The aim of this paper is to give an overview of domain adaptation and transfer learning with a specific view on visual applications. ...
Finally, we conclude the paper with a section where we relate domain adaptation to other machine learning solutions. ...
DA related to traditional ML methods Semi-supervised learning. ...
arXiv:1702.05374v2
fatcat:5va4oz4evjfhxgxddflpbb6pxi
Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities
[article]
2020
arXiv
pre-print
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It is also capable of teasing out more accurate insights from accident datasets than with traditional analysis tools, and this in turn can lead to better informed decision-making and more effective accident ...
We then look back and review the use of ML in reliability and safety applications. ...
Semi-supervised regression is a smaller area of research that its classification counterpart. ...
arXiv:2008.08221v1
fatcat:qhbkiepabfaz7afhctqutncheq
2020 Index IEEE Signal Processing Letters Vol. 27
2020
IEEE Signal Processing Letters
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Lin, Y., +,
LSP 2020 1095-1099
Cost-Sensitive Canonical Correlation Analysis for Semi-Supervised Multi-
View Learning. ...
., +, LSP 2020 1460-1464 Semi-Supervised Seq2seq Joint-Stochastic-Approximation Autoencoders With Applications to Semantic Parsing. ...
doi:10.1109/lsp.2021.3055468
fatcat:wfdtkv6fmngihjdqultujzv4by
2021 Index IEEE Transactions on Multimedia Vol. 23
2021
IEEE transactions on multimedia
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The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. ...
The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. ...
Li, Y., +, TMM 2021 810-822 Joint Input and Output Space Learning for Multi-Label Image Classification. ...
doi:10.1109/tmm.2022.3141947
fatcat:lil2nf3vd5ehbfgtslulu7y3lq
Semi-Autonomous Learning Algorithm for Remote Image Object Detection Based on Aggregation Area Instance Refinement
2021
Remote Sensing
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Finally, a regression-locating branch is further developed to refine the location of the object, which can be optimized jointly with regional classification. ...
Semi-autonomous learning for object detection has attracted more and more attention in recent years, which usually tends to find only one object instance with the highest score in each image. ...
Multi-Task Classification and Regression Branch After executing the above process, the pseudo GT can be obtained, and the subsequent classification task can be carried out in a fully supervised way. ...
doi:10.3390/rs13245065
fatcat:jehiwe2325c4hlglc3z37jocu4
CogDL: A Toolkit for Deep Learning on Graphs
[article]
2022
arXiv
pre-print
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We leverage CogDL to report and maintain benchmark results on the fundamental graph tasks such as node classification and graph classification, which can be reproduced and directly used by the community ...
Additionally, another important CogDL feature is its focus on ease of use with the goal of facilitating open, robust, and reproducible graph learning research. ...
ACKNOWLEDGMENTS The work is supported by the National Key R&D Program of China (2018YFB1402600), NSFC for Distinguished Young Scholar (61825602), and NSFC (61836013). ...
arXiv:2103.00959v3
fatcat:34lxb53rxjb2hnx5ramu5nomdq
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