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A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning [article]

Ha Quang Minh and Loris Bazzani and Vittorio Murino
2015 arXiv   pre-print
Our formulation encompasses both Vector-valued Manifold Regularization and Co-regularized Multi-view Learning, providing in particular a unifying framework linking these two important learning approaches  ...  This paper presents a general vector-valued reproducing kernel Hilbert spaces (RKHS) framework for the problem of learning an unknown functional dependency between a structured input space and a structured  ...  Our Contributions Our learning framework provides a unified formulation for Manifold Regularization and Co-regularized Multi-view Learning in the vector-valued setting.  ... 
arXiv:1401.8066v2 fatcat:jdnk4ltd6naffi6myojse4ayxe

Generalized Multi-manifold Graph Ensemble Embedding for Multi-View Dimensionality Reduction

Sumet Mehta
2020 Lahore Garrison University research journal of computer science and information technology  
Furthermore, to appropriately handle the intrinsic geometrical structure of the multi-view data and overcome the dimensionality curse, we propose a generalized multi-manifold graph ensemble embedding framework  ...  MLGEE aims to utilize multi-manifold graphs for the adjacency estimation with automatically weight each manifold to derive the integrated heterogeneous graph.  ...  Then these heterogeneous adjacent graphs are weighted to build a unified representation for multi-view manifold learning.  ... 
doi:10.54692/lgurjcsit.2020.0404109 fatcat:tzcha4tjbfhy5bv66f4jdswka4

Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds

Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst, Nikolai Nefedov
2014 IEEE Transactions on Signal Processing  
Our generic framework further extends to numerous analysis and learning problems that involve different types of information on graphs.  ...  In this paper, we address the problem of analyzing multi-layer graphs and propose methods for clustering the vertices by efficiently merging the information provided by the multiple modalities.  ...  The theory of Grassmann manifold provides a natural framework for such a problem.  ... 
doi:10.1109/tsp.2013.2295553 fatcat:4ugu5ogjxve53iesfgjtvt2kc4

Clustering on multi-layer graphs via subspace analysis on Grassmann manifolds

Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst, Nikolai Nefedov
2013 2013 IEEE Global Conference on Signal and Information Processing  
Our generic framework further extends to numerous analysis and learning problems that involve different types of information on graphs.  ...  This information can naturally be modeled by a set of weighted and undirected graphs that form a global multi-layer graph, where the common vertex set represents the entities and the edges on different  ...  The theory of Grassmann manifold provides a natural framework for such a problem.  ... 
doi:10.1109/globalsip.2013.6737060 dblp:conf/globalsip/DongFVN13 fatcat:crfgy54xazfjndsybg6psymdua

The Similarity-Consensus Regularized Multi-view Learning for Dimension Reduction [article]

Xiangzhu Meng, Huibing Wang, Lin Feng
2019 arXiv   pre-print
Consequently, constructing a multi-view learning framework with generalization and scalability, which could take advantage of multi-view information as much as possible, is extremely necessary but challenging  ...  To implement the above target, this paper proposes a novel multi-view learning framework based on similarity consensus, which makes full use of correlations among multi-view features while considering  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their insightful comments and the suggestions to significantly improve the quality of this paper.  ... 
arXiv:1911.07656v1 fatcat:32zfgasv7jf6pgvpurinpkgisq

Anchors Bring Ease: An Embarrassingly Simple Approach to Partial Multi-View Clustering

Jun Guo, Jiahui Ye
Specifically, APMC firstly integrates intra- and inter- view similarities through anchors. Then, spectral clustering is performed on the fused similarities to obtain a unified clustering result.  ...  To address this issue, this paper proposes a simple yet effective Anchorbased Partial Multi-view Clustering (APMC) method, which utilizes anchors to reconstruct instance-to-instance relationships for clustering  ...  Guo conceived the whole idea, built a basic implementation, and ran an early simulation. Ye conducted all experiments and analyzed the results. Two authors wrote the manuscript together.  ... 
doi:10.1609/aaai.v33i01.3301118 fatcat:2jz725k635cqdkgvisyj64dcby

Joint object recognition and pose estimation using a nonlinear view-invariant latent generative model

Amr Bakry, Tarek Elgaaly, Mohamed Elhoseiny, Ahmed Elgammal
2016 2016 IEEE Winter Conference on Applications of Computer Vision (WACV)  
In this paper we utilize this common topology between object manifolds by learning a low-dimensional latent space which non-linearly maps between a common unified manifold and the object manifold in the  ...  Different objects captured from the same set of viewpoints have manifolds with a common topology.  ...  Let {z k i ∈ R e , i = 1, · · · , n k } be the corresponding points on the unified manifold U, We learn a regularized mapping functions γ k (·) : R e → R D , which maps from U to each instance manifold  ... 
doi:10.1109/wacv.2016.7477655 dblp:conf/wacv/BakryEEE16 fatcat:vspwy634cbgtfn7sqs4opo7aiy

New Approaches in Multi-View Clustering [chapter]

Fanghua Ye, Zitai Chen, Hui Qian, Rui Li, Chuan Chen, Zibin Zheng
2018 Recent Applications in Data Clustering  
Besides, many other multi-view clustering methods can be unified into the frameworks of these five methods.  ...  Compared to single-view learning, multi-view learning has demonstrated plenty of advantages. Clustering has long been serving as a critical technique in data mining and machine learning.  ...  For instance, a pair-wise sparse subspace representation model for multi-view clustering proposed in [10] can be unified into the framework of matrix factorization.  ... 
doi:10.5772/intechopen.75598 fatcat:jniifuf4ync27fofz4fpbnfiia

A Survey on Concept Factorization: From Shallow to Deep Representation Learning [article]

Zhao Zhang, Yan Zhang, Mingliang Xu, Li Zhang, Yi Yang, Shuicheng Yan
2021 arXiv   pre-print
As a relatively new paradigm for representation learning, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade.  ...  Specifically, we first re-view the root CF method, and then explore the advancement of CF-based representation learning ranging from shallow to deep/multilayer cases.  ...  ACKNOWLEDGMENT This work is partially supported by the National Natural Science Foundation of China (61672365) and the Fundamental Research Funds for the Central Universities of China (JZ2019H-  ... 
arXiv:2007.15840v3 fatcat:ahun2mogmfapxe4mqhqlsakyku

Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering [article]

Shi-Xun Lina, Guo Zhongb, Ting Shu
2020 arXiv   pre-print
To address the above issues, we propose a novel multi-view subspace clustering method via simultaneously assigning weights for different features and capturing local information of data in view-specific  ...  Especially, a common cluster structure regularization is adopted to guarantee consistency among different views.  ...  more flexible local manifold structures of different views, we derives the proposed method, which unifies the featurewise weight learning and adaptive local structure learning into a framework.  ... 
arXiv:2007.12829v1 fatcat:mbagkzkh6zekdmfvda4eiii5k4

Multi-view Locality Low-rank Embedding for Dimension Reduction [article]

Lin Feng, Xiangzhu Meng, Huibing Wang
2019 arXiv   pre-print
Meanwhile, it aims to maintain the correlations and construct a suitable manifold space to capture the low-dimensional embedding for multi-view features.  ...  To tackle this problem, this paper proposes a novel multi-view dimension reduction method named Multi-view Locality Low-rank Embedding for Dimension Reduction (MvL2E).  ...  Acknowledgment The authors would like to thank the anonymous reviewers for their insightful comments and the suggestions to significantly improve the quality of this  ... 
arXiv:1905.08138v1 fatcat:dvu46twqonbktjo4a4iwzeilaa

Multiple Partitions Aligned Clustering [article]

Zhao Kang and Zipeng Guo and Shudong Huang and Siying Wang and Wenyu Chen and Yuanzhang Su and Zenglin Xu
2019 arXiv   pre-print
Finally, the basic partitions, weights, and consensus clustering are jointly learned in a unified framework.  ...  Moreover, a weight is assigned for each view to account for the clustering capacity differences of views.  ...  ZYGX2017KYQD177 and A03017023701012) and a 985 Project of UESTC (No. A1098531023601041) .  ... 
arXiv:1909.06008v1 fatcat:exttcqcagjaolaas6ilt5a5mhy

A Manifold Regularized Multi-Task Learning Model for IQ Prediction from Multiple fMRI Paradigms [article]

Li Xiao, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, and Yu-Ping Wang
2019 arXiv   pre-print
In this paper, we propose a new manifold regularized multi-task learning model by simultaneously considering between-subject and between-modality relationships.  ...  Besides employing a group-sparsity regularizer to jointly select a few common features across multiple tasks (modalities), we design a novel manifold regularizer to preserve the structure information both  ...  [26] and Lei et al. [27] studied a manifold regularized multi-task learning model by viewing the feature learning on each modality as one task.  ... 
arXiv:1901.05913v1 fatcat:cusuyndx35hnhnata5op4nmyxi

A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering

Khanh Luong, Richi Nayak
2020 2020 IEEE 36th International Conference on Data Engineering (ICDE)  
Illustration of manifold on each view. b. Illustration of a consensus manifold. A consensus and complementary learning model for multi-view data.  ...  PSLF [20] is a NMF-based multi-view clustering method designed for learning compatible and complementary information.  ... 
doi:10.1109/icde48307.2020.00080 dblp:conf/icde/LuongN20 fatcat:c2lx7nzm2zfj3h3zwicf35scym

Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction

Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changshui Zhang
2010 IEEE Transactions on Image Processing  
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the new data points.  ...  We also show that our proposed framework provides a unified view to explain and understand many semi-supervised, supervised and unsupervised dimension reduction techniques.  ...  Inspired by their work [1] , [33] , we propose a new manifold learning framework for dimension reduction in multi-class setting and our framework naturally unifies many existing dimension reduction methods  ... 
doi:10.1109/tip.2010.2044958 pmid:20215078 fatcat:bsf6sdcn35fotdbftotjb3ju54
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