High Dimensional Clustering with r-nets
نویسندگان
چکیده
منابع مشابه
High-dimensional approximate r-nets
The construction of r-nets offers a powerful tool in computational and metric geometry. We focus on high-dimensional spaces and present a new randomized algorithm which efficiently computes approximate r-nets with respect to Euclidean distance. For any fixed ǫ > 0, the approximation factor is 1 + ǫ and the complexity is polynomial in the dimension and subquadratic in the number of points. The a...
متن کاملHigh-dimensional data clustering
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 highdimensional data usually live in different low-dimensional subspaces hidden in the original space. This paper presents a family of Gaussian mixture models designed for highdimensional data which combine the ideas of subspace c...
متن کاملHigh-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust
The R package bclust is useful for clustering high-dimensional continuous data. The package uses a parametric spike-and-slab Bayesian model to downweight the effect of noise variables and to quantify the importance of each variable in agglomerative clustering. We take advantage of the existence of closed-form marginal distributions to estimate the model hyper-parameters using empirical Bayes, t...
متن کاملDeterministic clustering with data nets
We consider the K-clustering problem with the `2 distortion measure, also known as the problem of optimal fixed-rate vector quantizer design. We provide a deterministic approximation algorithm which works for all dimensions d and which, given a data set of size n, computes in time poly(K)(d/ε)n log log n+(d/ε) a solution of distortion at most 1+ε times optimal. The key tool is construction of a...
متن کاملHDclassif: An R Package for Model-Based Clustering and Discriminant Analysis of High-Dimensional Data
This paper presents the R package HDclassif which is devoted to the clustering and the discriminant analysis of high-dimensional data. The classification methods proposed in the package result from a new parametrization of the Gaussian mixture model which combines the idea of dimension reduction and model constraints on the covariance matrices. The supervised classification method using this pa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2019
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v33i01.33013207