ICA based on Split Generalized Gaussian
Independent Component Analysis (ICA) - one of the basic tools in data analysis - aims to find a coordinate system in which the components of the data are independent. Most popular ICA methods use kurtosis as a metric of non-Gaussianity to maximize, such as FastICA and JADE. However, their assumption of fourth-order moment (kurtosis) may not always be satisfied in practice. One of the possible solution is to use third-order moment (skewness) instead of kurtosis, which was applied in $ICA_{SG}$ and EcoICA. In this paper we present a competitive approach to ICA based on the Split Generalized Gaussian distribution (SGGD), which is well adapted to heavy-tailed as well as asymmetric data. Consequently, we obtain a method which works better than the classical approaches, in both cases: heavy tails and non-symmetric data. \end{abstract}
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
GES : Generalized Exponential Splatting for Efficient Radiance Field Rendering
Advancements in 3D Gaussian Splatting have significantly accelerated 3D reconstruction and generation. However it may require a large number of Gaussians which creates a substantial memory footprint. This paper intro…
3D ReconstructionNovel View SynthesisGES: Generalized Exponential Splatting for Efficient Radiance Field Rendering
Advancements in 3D Gaussian Splatting have significantly accelerated 3D reconstruction and generation. However, it may require a large number of Gaussians, which creates a substantial memory footprint. This paper introdu…
3D ReconstructionNovel View SynthesisGenerative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction
Generalized feed-forward Gaussian models have achieved significant progress in sparse-view 3D reconstruction by leveraging prior knowledge from large multi-view datasets. However, these models often struggle to represent…
3D ReconstructionSparse Point Cloud Patches Rendering via Splitting 2D Gaussians
Current learning-based methods predict NeRF or 3D Gaussians from point clouds to achieve photo-realistic rendering but still depend on categorical priors, dense point clouds, or additional refinements. Hence, we introduc…
NeRFReflection coupling for unadjusted generalized Hamiltonian Monte Carlo in the nonconvex stochastic gradient case
Contraction in Wasserstein 1-distance with explicit rates is established for generalized Hamiltonian Monte Carlo with stochastic gradients under possibly nonconvex conditions. The algorithms considered include splitting …