Fanhua Shang

According to our database1, Fanhua Shang authored at least 64 papers between 2011 and 2019.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2019
LRR for Subspace Segmentation via Tractable Schatten- $p$ Norm Minimization and Factorization.
IEEE Trans. Cybernetics, 2019

Local discriminative based sparse subspace learning for feature selection.
Pattern Recognition, 2019

signADAM: Learning Confidences for Deep Neural Networks.
CoRR, 2019

CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation.
CoRR, 2019

Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1.
CoRR, 2019

Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Direct Acceleration of SAGA using Sampled Negative Momentum.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Multi-Precision Quantized Neural Networks via Encoding Decomposition of {-1, +1}.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Fuzzy Double Trace Norm Minimization for Recommendation Systems.
IEEE Trans. Fuzzy Systems, 2018

Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications.
IEEE Trans. Pattern Anal. Mach. Intell., 2018

Semi-Supervised Tensorial Locally Linear Embedding for Feature Extraction Using PolSAR Data.
J. Sel. Topics Signal Processing, 2018

Bilinear Factor Matrix Norm Minimization for Robust PCA: Algorithms and Applications.
CoRR, 2018

ASVRG: Accelerated Proximal SVRG.
CoRR, 2018

A Unified Approximation Framework for Deep Neural Networks.
CoRR, 2018

A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates.
CoRR, 2018

Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization.
CoRR, 2018

Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization.
CoRR, 2018

VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning.
CoRR, 2018

A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates.
Proceedings of the 35th International Conference on Machine Learning, 2018

Guaranteed Sufficient Decrease for Stochastic Variance Reduced Gradient Optimization.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

ASVRG: Accelerated Proximal SVRG.
Proceedings of The 10th Asian Conference on Machine Learning, 2018

2017
LFTF: A Framework for Efficient Tensor Analytics at Scale.
PVLDB, 2017

Fast Stochastic Variance Reduced Gradient Method with Momentum Acceleration for Machine Learning.
CoRR, 2017

Variance Reduced Stochastic Gradient Descent with Sufficient Decrease.
CoRR, 2017

Larger is Better: The Effect of Learning Rates Enjoyed by Stochastic Optimization with Progressive Variance Reduction.
CoRR, 2017

Accelerated Variance Reduced Stochastic ADMM.
CoRR, 2017

Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Accelerated Variance Reduced Stochastic ADMM.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Generalized Higher Order Orthogonal Iteration for Tensor Learning and Decomposition.
IEEE Trans. Neural Netw. Learning Syst., 2016

Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization.
CoRR, 2016

Unified Scalable Equivalent Formulations for Schatten Quasi-Norms.
CoRR, 2016

Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Scalable Algorithms for Tractable Schatten Quasi-Norm Minimization.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data.
IEEE Trans. Cybernetics, 2015

Robust bilinear factorization with missing and grossly corrupted observations.
Inf. Sci., 2015

Regularized Orthogonal Tensor Decompositions for Multi-Relational Learning.
CoRR, 2015

2014
Double linear regressions for single labeled image per person face recognition.
Pattern Recognition, 2014

Maximum margin multiple-instance feature weighting.
Pattern Recognition, 2014

Sparse regularization discriminant analysis for face recognition.
Neurocomputing, 2014

Structured Low-Rank Matrix Factorization with Missing and Grossly Corrupted Observations.
CoRR, 2014

Generalized Higher-Order Tensor Decomposition via Parallel ADMM.
CoRR, 2014

Nuclear Norm Regularized Least Squares Optimization on Grassmannian Manifolds.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Factor Matrix Trace Norm Minimization for Low-Rank Tensor Completion.
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Recovering Low-Rank and Sparse Matrices via Robust Bilateral Factorization.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

Robust Principal Component Analysis with Missing Data.
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014

Generalized Higher-Order Tensor Decomposition via Parallel ADMM.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013
An Efficient Matrix Factorization Method for Tensor Completion.
IEEE Signal Process. Lett., 2013

Sparse coding and classifier ensemble based multi-instance learning for image categorization.
Signal Processing, 2013

Semi-supervised learning with nuclear norm regularization.
Pattern Recognition, 2013

An efficient matrix factorization based low-rank representation for subspace clustering.
Pattern Recognition, 2013

A fast tri-factorization method for low-rank matrix recovery and completion.
Pattern Recognition, 2013

An efficient matrix bi-factorization alternative optimization method for low-rank matrix recovery and completion.
Neural Networks, 2013

Fast Fisher Sparsity Preserving Projections.
Neural Computing and Applications, 2013

2012
An evidential reasoning based classification algorithm and its application for face recognition with class noise.
Pattern Recognition, 2012

Graph dual regularization non-negative matrix factorization for co-clustering.
Pattern Recognition, 2012

Fast affinity propagation clustering: A multilevel approach.
Pattern Recognition, 2012

Fast semi-supervised clustering with enhanced spectral embedding.
Pattern Recognition, 2012

Integrating Spectral Kernel Learning and Constraints in Semi-Supervised Classification.
Neural Processing Letters, 2012

Semi-supervised learning with mixed knowledge information.
Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Learning spectral embedding via iterative eigenvalue thresholding.
Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012

2011
Robust Positive semidefinite L-Isomap Ensemble.
Pattern Recognition Letters, 2011

Fast density-weighted low-rank approximation spectral clustering.
Data Min. Knowl. Discov., 2011

Learning Spectral Embedding for Semi-supervised Clustering.
Proceedings of the 11th IEEE International Conference on Data Mining, 2011


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