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Algorithms for an Efficient Tensor Biclustering [article]

Andriantsiory Dina Faneva, Mustapha Lebbah, Hanane Azzag, Gaël Beck
2019 arXiv   pre-print
This approach are based on spectral decomposition in order to build the desired biclusters. We evaluate the quality of the results from each algorithms with both synthetic and real data set.  ...  The tensor biclustering problem computes a subset of individuals and a subset of features whose signal trajectories over time lie in a low-dimensional subspace, modeling similarity among the signal trajectories  ...  We proposed two algorithms to solve this problem, tensor recursive and multiple bicluster.  ... 
arXiv:1903.04042v1 fatcat:yfchfnvzdnhxdml7suimemxrra

Identifying Multi-Dimensional Co-Clusters in Tensors Based on Hyperplane Detection in Singular Vector Spaces

Hongya Zhao, Debby D. Wang, Long Chen, Xinyu Liu, Hong Yan, Fabio Rapallo
2016 PLoS ONE  
Cheng and Church developed an efficient node-detection algorithm (CC) to find valuable submatrices in yeast or human experssion data, based on mean squared residue scores [2].  ...  Subsequently, a simple binary reference model was provided for comparing and validating biclustering methods [20] , and meanwhile a fast divide-and-conquer algorithm (BiMax: ch/  ...  Zhongying Zhao for providing the C. elegans lineage data. Visualization: XL. Writingoriginal draft: HZ DW LC. Writingreview & editing: LC HY.  ... 
doi:10.1371/journal.pone.0162293 pmid:27598575 pmcid:PMC5012624 fatcat:q3exv6js2recnl6ihadfazysya

Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation [article]

Emily Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, Rob Fergus
2014 arXiv   pre-print
We present techniques for speeding up the test-time evaluation of large convolutional networks, designed for object recognition tasks.  ...  Problem ( 4 ) is solved efficiently by performing alternate least squares on α, β and γ respectively, although more efficient algorithms can also be considered [7] .  ...  We now present two methods of improving this criterion while keeping the same efficient approximation algorithms.  ... 
arXiv:1404.0736v2 fatcat:zbsiprh7bjd5lmrszubfad4o5y

Towards a Unified Taxonomy of Biclustering Methods [article]

Dmitry I. Ignatov, Bruce W. Watson
2017 arXiv   pre-print
For instance, BiMax algorithm is aimed at finding biclusters, which are well-known for decades as formal concepts.  ...  Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains.  ...  The first author was also supported by Russian Foundation for Basic Research (grant #13-07-00504).  ... 
arXiv:1702.05376v1 fatcat:lzso53vqgvbphkjnmuk7uk6yb4

eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes

Zheng Chang, Zhenjia Wang, Cody Ashby, Chuan Zhou, Guojun Li, Shuzhong Zhang, Xiuzhen Huang
2014 Cancer Informatics  
Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data  ...  for patients of different subtypes.  ...  Acknowledgment We would like to thank Qin Ma and Juntao Liu for their helpful suggestions.  ... 
doi:10.4137/cin.s13777 pmid:25374455 pmcid:PMC4213194 fatcat:y546qmalcbadlaywyzuxvot3xm

Radiance Transfer Biclustering for Real-Time All-Frequency Biscale Rendering

Xin Sun, Qiming Hou, Zhong Ren, Kun Zhou, Baining Guo
2011 IEEE Transactions on Visualization and Computer Graphics  
The biclustering is directly applied on the radiance transfer represented in a pixel basis, on which the BTF is naturally defined.  ...  We present a real-time algorithm to render all-frequency radiance transfer at both macroscale and mesoscale.  ...  Recently, an all-frequency shadow algorithm for dynamic scenes has been proposed [30] .  ... 
doi:10.1109/tvcg.2010.58 pmid:20421683 fatcat:vvhohr7sqvfhjndkih7uo3w23i

Scalable biclustering — the future of big data exploration?

Patryk Orzechowski, Krzysztof Boryczko, Jason H Moore
2019 GigaScience  
We also try to explain why biclustering may soon become one of the standards for big data analytics.  ...  In this paper we discuss the caveats of biclustering and present its current challenges and guidelines for practitioners.  ...  ; TPU: tensor processing unit.  ... 
doi:10.1093/gigascience/giz078 pmid:31251324 pmcid:PMC6598466 fatcat:piz3g62475g2vkxyz5ehyf53s4

A New Algorithm for Convex Biclustering and Its Extension to the Compositional Data [article]

Binhuan Wang, Lanqiu Yao, Jiyuan Hu, Huilin Li
2021 arXiv   pre-print
biclusters required by existing convex biclustering algorithms.  ...  For example, biclustering for increasingly popular microbiome research data is under-applied possibly due to its compositional constraints for each sample.  ...  In addition, Zhou et al. (2020) developed a very efficient smoothing proximal gradient algorithm (Sproga) for convex clustering.  ... 
arXiv:2011.12182v2 fatcat:onylus2refexhmvanvlebuv5va

Mining Actionable Patterns of Road Mobility From Heterogeneous Traffic Data Using Biclustering

Francisco Neves, Anna C. Finamore, Sara C. Madeira, Rui Henriques
2021 IEEE transactions on intelligent transportation systems (Print)  
This work proposes a structured view on why, when and how to apply biclustering for mining traffic patterns of road mobility, a subject remaining largely unexplored up to date.  ...  Despite its relevance, the potentialities of applying biclustering in mobility domains remain unexplored.  ...  ACKNOWLEDGMENT The authors thank Câmara Municipal de Lisboa for data provision, support and valuable feedback, particularly Gabinete de Mobilidade and Centro de Operações Integrado.  ... 
doi:10.1109/tits.2021.3057240 fatcat:zgn5pjhsk5c4tn6wpa2rdwga2i

Biclustering Three-Dimensional Data Arrays With Plaid Models

Shawn Mankad, George Michailidis
2014 Journal of Computational And Graphical Statistics  
We present an efficient algorithm that first detects biclusters that exhibit abnormal behavior for some data matrices, and then estimates their responses over the entire data array.  ...  This paper describes an extension of a biclustering technique for visual exploration and pattern detection in such complex structured data.  ...  Runtimes are also provided in the supplementary material showing that the algorithm is computationally efficient and able to produce estimates in seconds.  ... 
doi:10.1080/10618600.2013.851608 fatcat:yk7dr4kxqnh3hcpkj5inwmdw54

Provable Convex Co-clustering of Tensors

Eric C Chi, Brian R Gaines, Will Wei Sun, Hua Zhou, Jian Yang
2020 Journal of machine learning research  
Moreover, there is a gap between statistical guarantees and computational efficiency for existing tensor clustering solutions due to the nature of their non-convex formulations.  ...  Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data.  ...  Acknowledgments The authors thank Xu Han for his help with the simulation experiments during the revision of this work.  ... 
pmid:33312074 pmcid:PMC7731944 fatcat:bjrmicvonfcupkwzctaprj44tq

Provable Convex Co-clustering of Tensors [article]

Eric C. Chi and Brian R. Gaines and Will Wei Sun and Hua Zhou and Jian Yang
2020 arXiv   pre-print
Moreover, there is a gap between statistical guarantees and computational efficiency for existing tensor clustering solutions due to the nature of their non-convex formulations.  ...  Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data.  ...  Acknowledgments The authors thank Xu Han for his help with the simulation experiments during the revision of this work.  ... 
arXiv:1803.06518v2 fatcat:tobmphiumjdvbbp7jj37y6aixa

A Unified Adaptive Co-identification Framework for High-D Expression Data [chapter]

Shuzhong Zhang, Kun Wang, Cody Ashby, Bilian Chen, Xiuzhen Huang
2012 Lecture Notes in Computer Science  
This induces increasing demands for effective methods for partitioning the data into biologically relevant groups.  ...  Testing results on yeast and Arabidopsis expression data are presented to demonstrate high efficiency of our approach and its effectiveness.  ...  We test the MBI approach for determining the number of co-groups: We first randomly Generic co-identification algorithm Input: A ∈ n 1 ×n 2 ×···×n d is an d-dimensional tensor, which holds the d-dimensional  ... 
doi:10.1007/978-3-642-34123-6_6 fatcat:jqhufcb3gra3lfeehtgsgd2qei

Classification of Time Series Gene Expression in Clinical Studies via Integration of Biological Network

Liwei Qian, Haoran Zheng, Hong Zhou, Ruibin Qin, Jinlong Li, Luonan Chen
2013 PLoS ONE  
Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction.  ...  Moreover, we compared our approach with several state-of-the-art algorithms and found that our method outperformed previous approaches with regard to various criteria.  ...  Michael Hecker for providing test dataset.  ... 
doi:10.1371/journal.pone.0058383 pmid:23516469 pmcid:PMC3596388 fatcat:tokxqowbq5eghk527533pjeruu

Pattern Mining Across Many Massive Biological Networks [chapter]

Wenyuan Li, Haiyan Hu, Yu Huang, Haifeng Li, Michael R. Mehan, Juan Nunez-Iglesias, Min Xu, Xifeng Yan, Xianghong Jasmine Zhou
2011 Functional Coherence of Molecular Networks in Bioinformatics  
To more efficiently analyze D, we construct second-order graphs S only for subgraphs of the summary graphĜ. Definition 6.5 (Coherent Graph).  ...  Finally, we introduce an advanced mathematical model suitable for analyzing multiple weighted networks.  ...  These clusters with perfect density can serve as seeds for our biclustering algorithm, which searches for larger biclusters that permit holes (i.e., 0's).  ... 
doi:10.1007/978-1-4614-0320-3_6 fatcat:57jcyt6itbe4vkbyzhwwvsgelq
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