High-dimensional data clustering

By Charles Bouveyron, Stéphane Girard, Cordelia Schmid. Computational Statistics & Data Analysis, 2006

426 citations recorded by the source corpus.

Clustering in high-dimensional spaces is a difficult problem which is recurrent in many domains, for example in image analysis. The difficulty is due to the fact that high-dimensional data usually live in different low-dimensional subspaces hidden in the original space. This paper presents a family of Gaussian mixture models designed for high-dimensional data which combine the ideas of dimension reduction and parsimonious modeling. These models give rise to a clustering method based on the Expectation-Maximization algorithm which is called High-Dimensional Data Clustering (HDDC). In order to correctly fit the data, HDDC estimates the specific subspace and the intrinsic dimension of each group. Our experiments on artificial and real datasets show that HDDC outperforms existing methods for clustering high-dimensional data

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/e61c9758a320a8733b75618c25f11234ac9a5395

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Citations in the corpus

Model-based clustering for multivariate functional data (2013, methodology): “…(2008) build a speciﬁc clustering algorithm based on parametric time series models, Bouveyron and Jacques (2011) extend the high-dimensional data clustering algorithm (HDDC, Bouveyron et al. (2007)) to the functional case and Jacques and Preda (2012) build a model-based clustering based on an…”
Heteroscedastic factor mixture analysis (2010, methodology): “More recently, Bouveyron et al. (2007) proposed a different parameterization of the generic component–covariance matrix i .”
Robust supervised classification with mixture models: Learning from data with uncertain labels (2009, methodology): “In [5], an EM-like approach is presented to estimate the parameters π j , µ j , a ij , b j and Q j .”
Variable Selection for Clustering with Gaussian Mixture Models (2009, neutral): “From (6), the theorem is demonstrated if it is proved that ∀(S, J) ∈ V1 ∪ V2, P (∆BIC(S, J) < 0) → n→∞ 0.”
Functional data clustering: a survey (2013, positive): “3), and some parsimony assumptions on the variance λjk are considered to define a family of parsimonious sub-models, quoted as funHDDC as an extension of the HDDC method for finite dimensional data [9].”
Clustering algorithms: A comparative approach (2016, methodology): “The algorithm is based on statistical models, with the assumption that all attributes may be relevant for clustering [91].”
A tutorial on task-parameterized movement learning and retrieval (2015, methodology): “The high-dimensional data clustering (HDDC) approach from [12] lies in this category of models addressing both subspace clustering and regularization.”
Model-based clustering of high-dimensional data: A review (2014, methodology): “Figure 1: Illustration of the modeling of subspace clustering methods in the case of two groups (illustration from [20]).”