Variable regularization affine projection sign algorithm in impulsive noisy environment
Affine projection sign algorithm (APSA) is an important adaptive filtering method to combat the impulsive noisy environment. However, the performance of APSA is poor, if its regularization parameter is not well chosen. We propose a variable regularization APSA (VR-APSA) approach, which adopts a gradient-based method to recursively reduce the norm of the \textsl{a priori} error vector. The resulting VR-APSA leverages the time correlation of both the input signal matrix and error vector to adjust the value of the regularization parameter. Simulation results confirm that our algorithm exhibits both fast convergence and small misadjustment properties.
Code (1)
Similar Papers 제목 키워드 기반
On the Correlation between the Noise and a Priori Error Vectors in Affine Projection Algorithms
This paper analyzes the correlation matrix between the a priori error and measurement noise vectors for affine projection algorithms (APA). This correlation stems from the dependence between the filter tap estimates and …
validL2-Stability Analysis of The Set-Membership Affine Projection Algorithm
In this letter, we study the local and the global robustness of the set-membership affine projection (SM-AP) algorithm. We demonstrate that the SM-AP algorithm has l2-stability. In fact, the SM-AP algorithm never diverge…
Temporal Anchoring in Deepening Embedding Spaces: Event-Indexed Projections, Drift, Convergence, and an Internal Computational Architecture
We develop an operator-theoretic framework for temporal anchoring in embedding spaces, modeled as drift maps interleaved with event-indexed blocks culminating in affine projections. We provide complete proofs for a varia…
Efficient Solvers for Sparse Subspace Clustering
Sparse subspace clustering (SSC) clusters $n$ points that lie near a union of low-dimensional subspaces. The SSC model expresses each point as a linear or affine combination of the other points, using either $\ell_1$ or …
ClusteringExplicit Group Sparse Projection with Applications to Deep Learning and NMF
We design a new sparse projection method for a set of vectors that guarantees a desired average sparsity level measured leveraging the popular Hoyer measure (an affine function of the ratio of the $\ell_1$ and $\ell_2$ n…
Network Pruning