Distributed Adaptive Learning with Multiple Kernels in Diffusion Networks
We propose an adaptive scheme for distributed learning of nonlinear functions by a network of nodes. The proposed algorithm consists of a local adaptation stage utilizing multiple kernels with projections onto hyperslabs and a diffusion stage to achieve consensus on the estimates over the whole network. Multiple kernels are incorporated to enhance the approximation of functions with several high and low frequency components common in practical scenarios. We provide a thorough convergence analysis of the proposed scheme based on the metric of the Cartesian product of multiple reproducing kernel Hilbert spaces. To this end, we introduce a modified consensus matrix considering this specific metric and prove its equivalence to the ordinary consensus matrix. Besides, the use of hyperslabs enables a significant reduction of the computational demand with only a minor loss in the performance. Numerical evaluations with synthetic and real data are conducted showing the efficacy of the proposed algorithm compared to the state of the art schemes.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Manifold Adaptive Multiple Kernel K-Means for Clustering
Multiple kernel methods based on k-means aims to integrate a group of kernels to improve the performance of kernel k-means clustering. However, we observe that most existing multiple kernel k-means methods exploit the no…
ClusteringAdaptive control of reaction-diffusion PDEs via neural operator-approximated gain kernels
Neural operator approximations of the gain kernels in PDE backstepping has emerged as a viable method for implementing controllers in real time. With such an approach, one approximates the gain kernel, which maps the pla…
parameter estimationDistributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View
Distributed adaptive filtering has been considered as an effective approach for data processing and estimation over distributed networks. Most existing distributed adaptive filtering algorithms focus on designing differe…
Diffusion Adaptation Over Clustered Multitask Networks Based on the Affine Projection Algorithm
Distributed adaptive networks achieve better estimation performance by exploiting temporal and as well spatial diversity while consuming few resources. Recent works have studied the single task distributed estimation pro…
DiversityAdaptive Surface Reconstruction With Multiscale Convolutional Kernels
We propose generalized convolutional kernels for 3D reconstruction with ConvNets from point clouds. Our method uses multiscale convolutional kernels that can be applied to adaptive grids as generated with octrees. In…
3D ReconstructionSurface Reconstruction