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Learning Robust Low-Rank Approximation for Crowdsourcing on Riemannian Manifold

Qian Li, Zhichao Wang, Gang Li, Yanan Cao, Gang Xiong, Li Guo
2017 Procedia Computer Science  
This paper proposes a Riemannian manifold optimization algorithm, ROLA (Robust Low-rank Approximation), to aggregate the labels from a novel perspective.  ...  This paper proposes a Riemannian manifold optimization algorithm, ROLA (Robust Low-rank Approximation), to aggregate the labels from a novel perspective.  ...  Analysis on Real-world Datasets Conclusion This paper proposes a novel optimization algorithm on matrix manifold, namely Robust Low-Rank Approximation for Crowdsourcing (ROLA), to aggregate the labels  ... 
doi:10.1016/j.procs.2017.05.179 fatcat:zu7jgrz64bgghea5zsvv4h3j5a

2020 Index IEEE Transactions on Signal Processing Vol. 68

2020 IEEE Transactions on Signal Processing  
., One-Step Prediction for Discrete Time-Varying Nonlinear Systems With Unknown Inputs and Correlated Noises; TSP  ...  ., +, TSP 2020 3209-3224 Efficient Low-Rank Approximation of Matrices Based on Randomized Piv- oted Decomposition.  ...  ., +, TSP 2020 5968-5982 Precise 3-D GNSS Attitude Determination Based on Riemannian Manifold Optimization Algorithms.  ... 
doi:10.1109/tsp.2021.3055469 fatcat:6uswtuxm5ba6zahdwh5atxhcsy

2021 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 32

2021 IEEE Transactions on Neural Networks and Learning Systems  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TNNLS Feb. 2021 535-545 Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices.  ...  ., +, TNNLS Feb. 2021 535-545 Generalized Learning Riemannian Space Quantization: A Case Study on Riemannian Manifold of SPD Matrices.  ... 
doi:10.1109/tnnls.2021.3134132 fatcat:2e7comcq2fhrziselptjubwjme

The Emerging Trends of Multi-Label Learning [article]

Weiwei Liu, Xiaobo Shen, Haobo Wang, Ivor W. Tsang
2020 arXiv   pre-print
Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep  ...  Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data.  ...  To achieve this goal, [226] explores the supervised multi-label learning algorithm in Riemannian diffusion tensor spaces, which considers diffusion tensors lying on the Log-Euclidean Riemannian manifold  ... 
arXiv:2011.11197v2 fatcat:hu6w4vgnwbcqrinrdfytmmjbjm

Automatic Symmetry Discovery with Lie Algebra Convolutional Network [article]

Nima Dehmamy, Robin Walters, Yanchen Liu, Dashun Wang, Rose Yu
2021 arXiv   pre-print
Existing equivariant neural networks require prior knowledge of the symmetry group and discretization for continuous groups.  ...  We discover direct connections between L-conv and physics: (1) group invariant loss generalizes field theory (2) Euler-Lagrange equation measures the robustness, and (3) equivariance leads to conservation  ...  We do this for the scrambled image tests, where we encode Li as low-rank matrices.  ... 
arXiv:2109.07103v2 fatcat:45et67py4vd4lfgsuqoknpp3be

Table of contents

2021 ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
Rao, University of California, San Diego, United States cxxxiii SPTM-24.4: AUTOMATIC REGISTRATION AND CLUSTERING OF TIME SERIES.........................................LOW-RANK ON GRAPHS PLUS TEMPORALLY  ...  SHARING INEXACT LOW-RANK SUBSPACE ............................... 3480 Xiaoqian Wang, Purdue University, United States; Feiping Nie, University of Texas at Arlington, United States MLSP-34.3: ON THE ADVERSARIAL  ... 
doi:10.1109/icassp39728.2021.9414617 fatcat:m5ugnnuk7nacbd6jr6gv2lsfby

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
DAY 4 -Jan 15, 2021 Guo, Jipeng; Sun, Yanfeng; Gao, Junbin; Hu, Yongli; Yin, Baocai 426 Low Rank Representation on Product Grassmann Manifolds for Multi-viewSubspace Clustering DAY 4 -Jan 15  ...  Grassmann Manifold via Double Low Rank Constraint DAY 2 -Jan 13, 2021 -DAY 2 -Jan 13, 2021 Live Fang, Jiansheng; Zhang, Xiaoqing; Hu, Yan; Xu, Yanwu; Yang, Ming; Liu, Jiang 19 PS T1.4 Probabilistic  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Multiscale entropy analysis of astronomical time series. Discovering subclusters of hybrid pulsators

J. Audenaert, A. Tkachenko
2022 Astronomy and Astrophysics  
We calculate the multiscale entropy for a set of Kepler light curves and simulated sine waves.  ...  It originates from the biomedical domain and was recently successfully used to characterize light curves as part of a supervised machine learning framework to classify stellar variability. Aims.  ...  The authors respectfully thank the anonymous referee for the thorough yet timely reviews of the manuscript; their enthousiasm is an important encouragement for us.  ... 
doi:10.1051/0004-6361/202243469 fatcat:f4jtq446mvdi3bcrv3ftqzawby

Multiscale entropy analysis of astronomical time series. Discovering subclusters of hybrid pulsators [article]

Jeroen Audenaert, Andrew Tkachenko
2022 arXiv   pre-print
We find that the multiscale entropy is a powerful tool for capturing variability patterns in stellar light curves.  ...  It originates from the biomedical domain and was recently successfully used to characterize light curves as part of a supervised machine learning framework to classify stellar variability.  ...  The authors respectfully thank the anonymous referee for the thorough yet timely reviews of the manuscript; their enthousiasm is an important encouragement for us.  ... 
arXiv:2206.13529v1 fatcat:weprqkppfnacdodypy6d66x26e

A Model-Free Time Series Segmentation Approach for Land Cover Change Detection

Ashish Garg, Lydia Manikonda, Shashank Kumar, Vikrant Krishna, Shyam Boriah, Michael S. Steinbach, Durga Toshnival, Vipin Kumar, Christopher Potter, Steven A. Klooster
2011 Conference on Intelligent Data Understanding  
CIDU is unique in creating a forum for the applications of data mining and machine learning to earth sciences, space sciences, and aerospace and engineering systems.  ...  The NASA Conference on Intelligent Data Understanding (CIDU) is applications-oriented, with a focus on Earth Sciences, Space Sciences, and Aerospace and Engineering Systems Applications.  ...  Julie Winkler for valuable discussion on statistical downscaling for climate change projection.  ... 
dblp:conf/cidu/GargMKKBSTKPK11 fatcat:yur5kmzxovbuxczf6lrix43viy

Spectral Inference Methods on Sparse Graphs: Theory and Applications [article]

Alaa Saade
2016 arXiv   pre-print
In this dissertation, we use methods derived from the statistical physics of disordered systems to design and study new algorithms for inference on graphs.  ...  Our focus is on spectral methods, based on certain eigenvectors of carefully chosen matrices, and sparse graphs, containing only a small amount of information.  ...  Still, we can endow the manifold of exponential models of the form (10.1) with a local Riemannian structure by showing [9, 123] The natural gradient approach enjoys desirable properties such as invariance  ... 
arXiv:1610.04337v1 fatcat:7dvgbovovnf4hpelj5peux6nti

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances [article]

Yan Wang, Wei Song, Wei Tao, Antonio Liotta, Dawei Yang, Xinlei Li, Shuyong Gao, Yixuan Sun, Weifeng Ge, Wei Zhang, Wenqiang Zhang
2022 arXiv   pre-print
baseline dataset, fusion strategies for multimodal affective analysis, and unsupervised learning models.  ...  Firstly, we introduce two typical emotion models followed by commonly used databases for affective computing.  ...  Adversarial learning is widely used to improve the robustness of models by augmenting data [206, 291] and cross-domain learning [156, 192] .  ... 
arXiv:2203.06935v3 fatcat:h4t3omkzjvcejn2kpvxns7n2qe

Visualization and Processing of Higher Order Descriptors for Multi-Valued Data

Bernhard Burgeth, Ingrid Hotz, Anna Vilanova Bartroli, Carl-Fredrik Westin
unpublished
This report documents the program and the outcomes of Dagstuhl Seminar 14082 "Visualization and Processing of Higher Order Descriptors for Multi-Valued Data".  ...  The focus of the seminar was to discuss modern and emerging methods for analysis and visualization of tensor and higher order descriptors from medical imaging and engineering applications.  ...  Acknowledgement The organizers thank all the attendees for their contributions and extend special thanks to the team of Schloss Dagstuhl for helping to make this seminar a success.  ... 
fatcat:v5qtj5jjjjdwnkwfgelatq4amy

Dagstuhl Reports, Volume 6, Issue 4, April 2016, Complete Issue [article]

2016
The organisers would like to express their gratitude to the participants and the Schloss Dagstuhl team for a productive and exciting seminar. Combination of Static Analysis and Learning  ...  We are also interested in exploring the robustness of the model when the frequency of re-optimisation is increased.  ...  When a few signals dominate, then low rank, i.e. low parametric, representation becomes possible.  ... 
doi:10.4230/dagrep.6.4 fatcat:27bnit22ive7hplns57eoojqtm

Uncertainty-aware visualization techniques [article]

Christoph Schulz, Universität Stuttgart
2022
One must learn how to interpret uncertainty-afflicted information. Accordingly, this thesis addresses three research questions: How can we identify and reason about uncertainty?  ...  Then, approaches to model uncertainty using descriptive statistics and unsupervised learning are discussed. Also, a model for validation and evaluation of visualization methods is proposed.  ...  UMAP also belongs to this class of methods but assumes that the data is uniformly distributed on a locally connected and constant Riemannian manifold while optimizing for H(p, q).  ... 
doi:10.18419/opus-12115 fatcat:la56gcmgkjgd3kyr45muwqnide
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