Robust Knowledge Discovery via Low-rank Modeling

It is always an attractive task to discover knowledge for various learning problems; however, this knowledge discovery and maintenance process usually suffers from noise, incompleteness or knowledge domain mismatch. Thus, robust knowledge discovery by removing the noisy features or samples, complementing incomplete data, and mitigating the distribution difference becomes the key. Along this line of research, low-rank modeling is widely-used to solve these challenges. This survey covers the topic of: (1) robust knowledge recovery, (2) robust knowledge transfer, (3) robust knowledge fusion, centered around several major applications. First of all, we deliver a unified formulation for robust knowledge discovery based on a given dataset. Second, we discuss robust knowledge transfer and fusion given multiple datasets with different knowledge flows, followed by practical challenges, model variations, and remarks. Finally, we highlight future research of robust knowledge discovery for incomplete, unbalance, large-scale data analysis. This would benefit AI community from literature review to future direction.

[1]  Ming Shao,et al.  Deep Low-Rank Coding for Transfer Learning , 2015, IJCAI.

[2]  Changsheng Xu,et al.  General Subspace Learning With Corrupted Training Data Via Graph Embedding , 2013, IEEE Transactions on Image Processing.

[3]  Dinggang Shen,et al.  Multi-View Missing Data Completion , 2018, IEEE Transactions on Knowledge and Data Engineering.

[4]  Ming Shao,et al.  Deep Robust Encoder Through Locality Preserving Low-Rank Dictionary , 2016, ECCV.

[5]  Zhengming Ding,et al.  Robust Multiview Data Analysis Through Collective Low-Rank Subspace , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Ming Shao,et al.  Latent Low-Rank Transfer Subspace Learning for Missing Modality Recognition , 2014, AAAI.

[7]  Stefanos Zafeiriou,et al.  Robust Correlated and Individual Component Analysis , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Yong Yu,et al.  Robust Recovery of Subspace Structures by Low-Rank Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Trevor J. Hastie,et al.  Matrix completion and low-rank SVD via fast alternating least squares , 2014, J. Mach. Learn. Res..

[10]  Yun Fu,et al.  Latent Discriminant Subspace Representations for Multi-View Outlier Detection , 2018, AAAI.

[11]  Hachem Kadri,et al.  Low-Rank Regression with Tensor Responses , 2016, NIPS.

[12]  Ming Shao,et al.  Incomplete Multisource Transfer Learning , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Ming Shao,et al.  Efficient Transfer Feature Learning and Its Applications on Social Media , 2016 .

[14]  Jian Yang,et al.  Double Nuclear Norm-Based Matrix Decomposition for Occluded Image Recovery and Background Modeling , 2015, IEEE Transactions on Image Processing.

[15]  Ming Shao,et al.  Spectral Bisection Tree Guided Deep Adaptive Exemplar Autoencoder for Unsupervised Domain Adaptation , 2016, AAAI.

[16]  Yun Fu,et al.  Learning Robust and Discriminative Subspace With Low-Rank Constraints , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Shuicheng Yan,et al.  Latent Low-Rank Representation for subspace segmentation and feature extraction , 2011, 2011 International Conference on Computer Vision.

[18]  Fernando De la Torre,et al.  Robust Regression , 2016, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Yun Fu,et al.  From Ensemble Clustering to Multi-View Clustering , 2017, IJCAI.

[20]  Ming Shao,et al.  Low-Rank Embedded Ensemble Semantic Dictionary for Zero-Shot Learning , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Yun Fu,et al.  Deep Transfer Low-Rank Coding for Cross-Domain Learning , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Lin Wu,et al.  Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Dong Liu,et al.  Robust visual domain adaptation with low-rank reconstruction , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Wei Zhang,et al.  Consistent and Specific Multi-View Subspace Clustering , 2018, AAAI.

[25]  Ming Shao,et al.  Generalized Transfer Subspace Learning Through Low-Rank Constraint , 2014, International Journal of Computer Vision.

[26]  Ming Shao,et al.  Generative Zero-Shot Learning via Low-Rank Embedded Semantic Dictionary , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  Shuicheng Yan,et al.  Multi-task low-rank affinity pursuit for image segmentation , 2011, 2011 International Conference on Computer Vision.

[28]  Yuhong Guo,et al.  Semi-Supervised Multi-Label Learning with Incomplete Labels , 2015, IJCAI.

[29]  Junbin Gao,et al.  Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering , 2015, IEEE Transactions on Image Processing.

[30]  Daming Shi,et al.  Low-Rank-Sparse Subspace Representation for Robust Regression , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Lei Du,et al.  Robust Multi-View Spectral Clustering via Low-Rank and Sparse Decomposition , 2014, AAAI.

[32]  Xindong Wu,et al.  Scalable Nearest Neighbor Sparse Graph Approximation by Exploiting Graph Structure , 2016, IEEE Transactions on Big Data.

[33]  Yun Fu,et al.  Robust Multi-View Subspace Learning through Dual Low-Rank Decompositions , 2016, AAAI.

[34]  Yong Luo,et al.  Low-Rank Multi-View Learning in Matrix Completion for Multi-Label Image Classification , 2015, AAAI.

[35]  Ming Shao,et al.  Robust Discriminative Metric Learning for Image Representation , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[36]  Yi Ma,et al.  Robust principal component analysis? , 2009, JACM.

[37]  Jian Yu,et al.  Semi-supervised low-rank mapping learning for multi-label classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).