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High-dimensional data clustering
2007
Computational Statistics & Data Analysis
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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
2013
Journal of Chemometrics
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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
2013
Journal of Chemometrics
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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
2016
2016 6th International Conference on Computer and Knowledge Engineering (ICCKE)
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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]
2019
arXiv
pre-print
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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]
2016
arXiv
pre-print
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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
2020
Advances in Data Analysis and Classification
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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)
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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]
2016
arXiv
pre-print
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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
2016
IEEE Transactions on Image Processing
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2016 pre-print arXiv:1601.01060v1 fatcat:pqds5kfkovgybkgzagmll6y2fe
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
2014
Computer Science Review
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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)
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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]
2020
arXiv
pre-print
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2019 pre-print arXiv:1909.10832v1 fatcat:7duu7mzwqzdclp4cjcgd7smmky
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]
2015
arXiv
pre-print
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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
2007
Visual Communications and Image Processing 2007
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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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