Zobrazeno 1 - 10
of 161 063
pro vyhledávání: '"High dimensional data"'
Finding meaningful distances between high-dimensional data samples is an important scientific task. To this end, we propose a new tree-Wasserstein distance (TWD) for high-dimensional data with two key aspects. First, our TWD is specifically designed
Externí odkaz:
http://arxiv.org/abs/2410.21107
Data stream clustering reveals patterns within continuously arriving, potentially unbounded data sequences. Numerous data stream algorithms have been proposed to cluster data streams. The existing data stream clustering algorithms still face signific
Externí odkaz:
http://arxiv.org/abs/2409.04698
Statistical inference is challenging in high-dimensional data analysis. Existing post-selection inference requires an explicitly specified regression model as well as sparsity in the regression model. The performance of such procedures can be poor un
Externí odkaz:
http://arxiv.org/abs/2410.19031
Fuzzy Neural Networks (FNNs) are effective machine learning models for classification tasks, commonly based on the Takagi-Sugeno-Kang (TSK) fuzzy system. However, when faced with high-dimensional data, especially with noise, FNNs encounter challenges
Externí odkaz:
http://arxiv.org/abs/2410.13390
Autor:
Feng, Long
This article focuses on the robust principal component analysis (PCA) of high-dimensional data with elliptical distributions. We investigate the PCA of the sample spatial-sign covariance matrix in both nonsparse and sparse contexts, referring to them
Externí odkaz:
http://arxiv.org/abs/2409.13267
Autor:
Butsch, Lucas, Fasen-Hartmann, Vicky
In multivariate extreme value analysis, the estimation of the dependence structure in extremes is a challenging task, especially in the context of high-dimensional data. Therefore, a common approach is to reduce the model dimension by considering onl
Externí odkaz:
http://arxiv.org/abs/2409.10174
In this paper, we investigate sphericity testing in high-dimensional settings, where existing methods primarily rely on sum-type test procedures that often underperform under sparse alternatives. To address this limitation, we propose two max-type te
Externí odkaz:
http://arxiv.org/abs/2410.24094