Our work is focused on the joint sparsity recovery problem where the common sparsity pattern is corrupted by Poisson noise. We formulate the confidence-constrained optimization problem in both least squares (LS) and maximum likelihood (ML) frameworks and study the conditions for perfect reconstruction of the original r…
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10 results for “row-sparsity”
In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method for recovery of common row sparsity MMV signals, where a pattern-coupled hierarchical Gaussian prior model is introduced to characterize bo…
The paper develops inference methods for high-dimensional multi-task regression with row-sparse coefficients.
problem Inference for high-dimensional multi-task regression with unknown coefficient matrix under row-sparsity.
method Proposes chi-square and normal inference methodologies using MT Lasso with de-biasing scheme and interaction matrix.
result Derives asymptotic normal and chi-square distribution results for valid confidence intervals and ellipsoids.
Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i.…
We study sparse principal components analysis in high dimensions, where (the number of variables) can be much larger than (the number of observations), and analyze the problem of estimating the subspace spanned by the principal eigenvectors of the population covariance matrix. We introduce two complementary not…
Given the observation of a high-dimensional Ornstein-Uhlenbeck (OU) process in continuous time, we proceed to the inference of the drift parameter under a row-sparsity assumption. Towards that aim, we consider the negative log-likelihood of the process, penalized by an -penalization (Lasso and Adaptive Lasso). …
Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learningstat.ML
We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally correlated. Existing algorithms do not consider such temporal correlations and thus their performance degrades significantly with the correlations. I…
Proposes SROF for row-wise fusion in federated learning for multivariate responses.
problem Heterogeneous client models with shared variable-level structure.
method Sparse Row-wise Fusion (SROF) regularizer and RowFed algorithm.
result Empirically shows consistent error reduction and stronger variable-level cluster recovery.
Proposes RLAR for efficient labeled data classification with robust margin and manifold structure.
problem Clear margin representation and data manifold structure difficulty in linear discriminant methods.
method Introduces retargeted regression for adaptive margin learning and locality-aware strategy for compact data manifold.
result RLAR outperforms state-of-the-art approaches in UCI and benchmark data sets.
Finding an informative subset of a large collection of data points or models is at the center of many problems in computer vision, recommender systems, bio/health informatics as well as image and natural language processing. Given pairwise dissimilarities between the elements of a `source set' and a `target set,' we co…