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High-dimensional data clustering

C. Bouveyron, S. Girard, C. Schmid
2007 Computational Statistics & Data Analysis  
This paper presents a family of Gaussian mixture models designed for high-dimensional data which combine the ideas of dimension reduction and parsimonious modeling.  ...  In order to correctly fit the data, HDDC estimates the specific subspace and the intrinsic dimension of each group.  ...  Acknowledgments This work was supported by the French department of research through the ACI Masse de données (Movistar project).  ... 
doi:10.1016/j.csda.2007.02.009 fatcat:q7fix4bghrcwtjuryhwnby6kzm

Probabilistic model-based discriminant analysis and clustering methods in chemometrics

Charles Bouveyron
2013 Journal of Chemometrics  
This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modeling, subspace classification methods and classification methods based  ...  Model-based techniques for discriminant analysis and clustering are popular tools which are renowned for their probabilistic foundations and their flexibility.  ...  Mixture of high-dimensional Gaussian mixture models (HD-GMM) In a slightly different context, Bouveyron et al.  ... 
doi:10.1002/cem.2563 fatcat:bcowqw3z2zbtdd3r75gacgeuku

Probabilistic model-based discriminant analysis and clustering methods in chemometrics

Charles Bouveyron
2013 Journal of Chemometrics  
This work presents a comprehensive review of these recent approaches, including regularization-based techniques, parsimonious modeling, subspace classification methods and classification methods based  ...  Model-based techniques for discriminant analysis and clustering are popular tools which are renowned for their probabilistic foundations and their flexibility.  ...  Mixture of high-dimensional Gaussian mixture models (HD-GMM) In a slightly different context, Bouveyron et al.  ... 
doi:10.1002/cem.2560 fatcat:yyxavwgv6vhrjgse3yje2nqti4

Weighted semi-supervised manifold clustering via sparse representation

Amir Abedi, Reza Monsefi, Davood Zabih zadeh
2016 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)  
In this paper we propose a method for constrained clustering that take advantage of pairwise constraints.  ...  It first solves an optimization program to construct an affinity matrix according to pairwise constraints and manifold structure of data, then applies spectral clustering to find data clusters.  ...  ACKNOWLEDGMENT This work is supported by machine learning laboratory in engineering college of Ferdowsi university of Mashhad.  ... 
doi:10.1109/iccke.2016.7802146 fatcat:abmywhbdzrgp7lfxplhunibegy

How bettering the best? Answers via blending models and cluster formulations in density-based clustering [article]

Alessandro Casa, Luca Scrucca, Giovanna Menardi
2019 arXiv   pre-print
As opposed to the standard case, where clusters are associated to the components of the selected mixture model, we define partitions by borrowing the modal, or nonparametric, formulation of the clustering  ...  We propose an ensemble clustering approach that circumvents the single best model paradigm, while improving stability and robustness of the partitions.  ...  Deep Gaussian Mixture Models are networks of multiple layers of latent variables distributed as a mixture of Gaussian densities.  ... 
arXiv:1911.06726v1 fatcat:rtozjbge5reyvphee5rx7bix6u

Online Data Thinning via Multi-Subspace Tracking [article]

Xin Jiang, Rebecca Willett
2016 arXiv   pre-print
At the heart of this proposed approach is an online anomaly detection method based on dynamic, low-rank Gaussian mixture models.  ...  The low-rank modeling mitigates the curse of dimensionality associated with anomaly detection for high-dimensional data, and recent advances in subspace clustering and subspace tracking allow the proposed  ...  Subspace clustering and tracking The proposed method is also closely related to the subspace clustering and tracking algorithms. Subspace clustering is a relatively new, but vibrant field of study.  ... 
arXiv:1609.03544v1 fatcat:6lz5cm6eg5bp3folmdazxth5f4

Better than the best? Answers via model ensemble in density-based clustering

Alessandro Casa, Luca Scrucca, Giovanna Menardi
2020 Advances in Data Analysis and Classification  
We propose an ensemble clustering approach that circumvents the single best model paradigm, while improving stability and robustness of the partitions.  ...  In this work we focus on the model-based clustering formulation, where a plethora of mixture models, with different number of components and parametrizations, is typically estimated.  ...  Deep Gaussian Mixture Models are networks of multiple layers of latent variables distributed as a mixture of Gaussian densities.  ... 
doi:10.1007/s11634-020-00423-6 fatcat:h7uphslverfczfvdiuqllgisxe

SAM 2020 Author Index

2020 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)  
Greedy Neighbor Selection SS13.2 Sparse Subspace Clustering with Linear Subspace- Neighborhood-Preserving Data Embedding Ge, Mengmeng SS07.3 Mainlobe Jamming Suppression Via Independent Component  ...  Sonar in the Presence of Impulse Noise Li, Songlin R07.3 MIMO Radar Localization of Targets Behind L- shaped Corners Li, Wen-Hsian SS13.2 Sparse Subspace Clustering with Linear Subspace- Neighborhood-Preserving  ... 
doi:10.1109/sam48682.2020.9104397 fatcat:cfp5gsikrzabhhcnkalahjkxze

Subspace clustering based on low rank representation and weighted nuclear norm minimization [article]

Yu Song, Yiquan Wu
2016 arXiv   pre-print
Subspace clustering refers to the problem of segmenting a set of data points approximately drawn from a union of multiple linear subspaces.  ...  Aiming at the subspace clustering problem, various subspace clustering algorithms have been proposed and low rank representation based subspace clustering is a very promising and efficient subspace clustering  ...  ALC looks for the segmentation of the data that minimizes the coding length needed to fit the points with a mixture of degenerate Gaussians up to a given distortion [12] .  ... 
arXiv:1610.03604v3 fatcat:p5qifxnunva65fzygn4lowfjke

Robust Low-Rank Matrix Factorization Under General Mixture Noise Distributions

Xiangyong Cao, Qian Zhao, Deyu Meng, Yang Chen, Zongben Xu
2016 IEEE Transactions on Image Processing  
Each component in this mixture is adapted from a series of preliminary super- or sub-Gaussian candidates.  ...  To make LRMF capable of adapting more complex noise, this paper proposes a new LRMF model by assuming noise as Mixture of Exponential Power (MoEP) distributions and proposes a penalized MoEP (PMoEP) model  ...  Specifically, we encode the noise as a mixture distribution of a series of sub-and super-Gaussians (i.e., general exponential power (EP) distribution), and formulate LRMF as a penalized MLE model, called  ... 
doi:10.1109/tip.2016.2593343 pmid:27448358 fatcat:jz5b4to3afbrhaviqdjcsskiou

Traditional and recent approaches in background modeling for foreground detection: An overview

Thierry Bouwmans
2014 Computer Science Review  
We have classified them in terms of the mathematical models used and we have discussed them in terms of the critical situations that they claim to handle.  ...  So, the purpose of this paper is to provide a complete survey of the traditional and recent approaches. First, we categorize the different approaches found in the literature.  ...  Robust subspace models The background and the foreground are separated via a robust subspace model which is based on a low-rank and sparse decomposition.  ... 
doi:10.1016/j.cosrev.2014.04.001 fatcat:wccwuwltk5fr7lsgmsu5qbxclm

[SAM 2020 Title Page]

2020 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)  
Subspace 134 1570618141 Robust Adaptive Beamforming of LFM Signals Based on Interference-plus-Noise Covariance Matrix Reconstruction in Fractional Fourier Domain 135 1570617198 Robust Adaptive Monopulse  ...  Clustering with Linear Subspace-Neighborhood-Preserving Data Embedding 144 1570620539 Spectral Algorithm for Shared Low-rank Matrix Regressions 145 1570620900 Study on Coding Scheme with EPC-MIMO Radar  ... 
doi:10.1109/sam48682.2020.9104267 fatcat:erntqdmhdrdspcrkvjowtplyyq

High-dimensional clustering via Random Projections [article]

Laura Anderlucci, Francesca Fortunato, Angela Montanari
2020 arXiv   pre-print
The final partition is obtained by aggregating the clusters found in the projections via consensus. The performances of the method are assessed on both real and simulated datasets.  ...  Specifically, we propose to generate a set of low dimensional independent random projections and to perform model-based clustering on each of them.  ...  This paper is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-17-1-010.  ... 
arXiv:1909.10832v2 fatcat:kmemknrwzrdrbkpupbbik6nvre

Gaussian Mixture Models with Component Means Constrained in Pre-selected Subspaces [article]

Mu Qiao, Jia Li
2015 arXiv   pre-print
We investigate a Gaussian mixture model (GMM) with component means constrained in a pre-selected subspace. Applications to classification and clustering are explored.  ...  Experiments on real and simulated data sets are conducted to examine several ways of determining the subspace and to compare with the reduced rank mixture discriminant analysis (MDA).  ...  Introduction The Gaussian mixture model (GMM) is a popular and effective tool for clustering and classification.  ... 
arXiv:1508.06388v1 fatcat:agpybq3o6vbzre46hejix55gry

Segmentation of multivariate mixed data via lossy coding and compression

Harm Derksen, Yi Ma, Wei Hong, John Wright, Chang Wen Chen, Dan Schonfeld, Jiebo Luo
2007 Visual Communications and Image Processing 2007  
In this paper, based on ideas from lossy data coding and compression, we present a simple but effective technique for segmenting multivariate mixed data that are drawn from a mixture of Gaussian distributions  ...  Simulation results reveal intriguing phase-transition-like behaviors of the number of segments when changing the level of distortion or the amount of outliers.  ...  about MDL and mixture distributions.  ... 
doi:10.1117/12.714912 dblp:conf/vcip/DerksenMHW07 fatcat:dwfe3wqmxfgcplocbkm6qynhi4
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