Papers subspace methods
“subspace methods” 태그가 달린 논문 49편 · 필터 해제
Output behavior equivalence and simultaneous subspace identification of systems and faults
We address the problem of identifying a system subject to additive faults, while simultaneously reconstructing the fault signal via subspace methods. We do not require nominal data for the identification, neither do we i…
subspace methodsBOLT: Block-Orthonormal Lanczos for Trace estimation of matrix functions
Efficient matrix trace estimation is essential for scalable computation of log-determinants, matrix norms, and distributional divergences. In many large-scale applications, the matrices involved are too large to store or…
subspace methodsScalable Computations for Generalized Mixed Effects Models with Crossed Random Effects Using Krylov Subspace Methods
Mixed effects models are widely used for modeling data with hierarchically grouped structures and high-cardinality categorical predictor variables. However, for high-dimensional crossed random effects, current standard c…
subspace methodsNear Field Localization via AI-Aided Subspace Methods
The increasing demands for high-throughput and energy-efficient wireless communications are driving the adoption of extremely large antennas operating at high-frequency bands. In these regimes, multiple users will reside…
subspace methodsPLAIN: Scalable Estimation Architecture for Integrated Sensing and Communication
Integrated sensing and communication (ISAC) is envisioned be to one of the paradigms upon which next-generation mobile networks will be built, extending localization and tracking capabilities, as well as giving birth to …
compressed sensingIntegrated sensing and communicationISACmultidimensional harmonic retrieval+4Randomized Kaczmarz Methods with Beyond-Krylov Convergence
Randomized Kaczmarz methods form a family of linear system solvers which converge by repeatedly projecting their iterates onto randomly sampled equations. While effective in some contexts, such as highly over-determined …
subspace methodsDistributionally Robust Adaptive Beamforming
As a fundamental technique in array signal processing, beamforming plays a crucial role in amplifying signals of interest (SoI) while mitigating interference plus noise (IPN). When uncertainties exist in the signal model…
subspace methodsLine Spectral Analysis Using the G-Filter: An Atomic Norm Minimization Approach
The area of spectral analysis has a traditional dichotomy between continuous spectra (spectral densities) which correspond to purely nondeterministic processes, and line spectra (Dirac impulses) which represent sinusoids…
compressed sensingsubspace methodsDeep Koopman-layered Model with Universal Property Based on Toeplitz Matrices
We propose deep Koopman-layered models with learnable parameters in the form of Toeplitz matrices for analyzing the transition of the dynamics of time-series data. The proposed model has both theoretical solidness and fl…
subspace methodsTime SeriesLearning incomplete factorization preconditioners for GMRES
Incomplete LU factorizations of sparse matrices are widely used as preconditioners in Krylov subspace methods to speed up solving linear systems. Unfortunately, computing the preconditioner itself can be time-consuming a…
Graph Neural Networksubspace methodsSubspace Representation Learning for Sparse Linear Arrays to Localize More Sources than Sensors: A Deep Learning Methodology
Localizing more sources than sensors with a sparse linear array (SLA) has long relied on minimizing a distance between two covariance matrices and recent algorithms often utilize semidefinite programming (SDP). Although …
Representation Learningsubspace methodsUNLEARN Efficient Removal of Knowledge in Large Language Models
Given the prevalence of large language models (LLMs) and the prohibitive cost of training these models from scratch, dynamically forgetting specific knowledge e.g., private or proprietary, without retraining the model ha…
subspace methodsGridless Parameter Estimation in Partly Calibrated Rectangular Arrays
Spatial frequency estimation from a mixture of noisy sinusoids finds applications in various fields. While subspace-based methods offer cost-effective super-resolution parameter estimation, they demand precise array cali…
parameter estimationsubspace methodsSuper-ResolutionRandom Subspace Local Projections
We show how random subspace methods can be adapted to estimating local projections with many controls. Random subspace methods have their roots in the machine learning literature and are implemented by averaging over reg…
Dimensionality Reductionsubspace methodsSparse Spatial Smoothing: Reduced Complexity and Improved Beamforming Gain via Sparse Sub-Arrays
This paper addresses the problem of single snapshot Direction-of-Arrival (DOA) estimation, which is of great importance in a wide-range of applications including automotive radar. A popular approach to achieving high ang…
subspace methodsAn L-BFGS-B approach for linear and nonlinear system identification under $\ell_1$ and group-Lasso regularization
In this paper, we propose a very efficient numerical method based on the L-BFGS-B algorithm for identifying linear and nonlinear discrete-time state-space models, possibly under $\ell_1$ and group-Lasso regularization fo…
State Space Modelssubspace methodsKrylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate
Second-order optimization methods, such as cubic regularized Newton methods, are known for their rapid convergence rates; nevertheless, they become impractical in high-dimensional problems due to their substantial memory…
Second-order methodssubspace methodsSCAN-MUSIC: An Efficient Super-resolution Algorithm for Single Snapshot Wide-band Line Spectral Estimation
We propose an efficient algorithm for reconstructing one-dimensional wide-band line spectra from their Fourier data in a bounded interval $[-\Omega,\Omega]$. While traditional subspace methods such as MUSIC achieve super…
subspace methodsSuper-ResolutionLarge-scale magnetic field maps using structured kernel interpolation for Gaussian process regression
We present a mapping algorithm to compute large-scale magnetic field maps in indoor environments with approximate Gaussian process (GP) regression. Mapping the spatial variations in the ambient magnetic field can be used…
regressionsubspace methodsUncertainty QuantificationOne-Bit-Aided Modulo Sampling for DOA Estimation
Modulo sampling has recently drawn a great deal of attention for cutting-edge applications, due to overcoming the barrier of information loss through sensor saturation and clipping. This is a significant problem, especia…
DecoderQuantizationsubspace methods