The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of L1 minimization and works well for image recognition satisfying a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC und…
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14 results for “l1-minimization”
New RNN reconstructs video frames from sparse measurements.
problem Sequential signal reconstruction from compressive measurements.
method Unfolding proximal gradient method for l1-l1 minimization.
result Outperforms state-of-the-art RNN models in video frame reconstruction.
Bagging improves sparse regression performance, especially with reduced sampling ratios.
problem Improving sparse regression performance in low measurement scenarios.
method Generalized Bagging with various bootstrap sampling ratios.
result Bagging outperforms L1 minimization and Bolasso in challenging sparse regression cases.
A neural network solves dictionary learning problems efficiently.
problem Solving dictionary learning problems efficiently.
method Combining top-down feedback and contrastive learning with spiking neurons.
result True gradients for learning are provably computable by individual neurons.
Identifying context-specific entity networks from aggregated data is an important task, arising often in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several co…
We address the problem of Compressed Sensing (CS) with side information. Namely, when reconstructing a target CS signal, we assume access to a similar signal. This additional knowledge, the side information, is integrated into CS via L1-L1 and L1-L2 minimization. We then provide lower bounds on the number of measuremen…
A Convex Program for Mixed Linear Regression with a Recovery Guarantee for Well-Separated Datamath.OC
We introduce a convex approach for mixed linear regression over features. This approach is a second-order cone program, based on L1 minimization, which assigns an estimate regression coefficient in for each data point. These estimates can then be clustered using, for example, -means. For problem…
Solving inverse problems with iterative algorithms is popular, especially for large data. Due to time constraints, the number of possible iterations is usually limited, potentially affecting the achievable accuracy. Given an error one is willing to tolerate, an important question is whether it is possible to modify the…
The events of the last few years revealed an acute need for tools to systematically model and analyze large financial networks. Many applications of such tools include the forecasting of systemic failures and analyzing probable effects of economic policy decisions. We consider optimizing the amount and structure of a b…
Sparse Generalized Canonical Correlation Analysis: Distributed Alternating Iteration based Approachcs.LG
Proposes GCCA for detecting latent relations in multiview data with sparse structures.
problem Sparse CCA limitations for multiple datasets.
method Developed a GCCA algorithm based on distributed alternating iteration approach.
result Demonstrated effectiveness on synthetic and real-world datasets.
Despite the importance of sparsity signal models and the increasing prevalence of high-dimensional streaming data, there are relatively few algorithms for dynamic filtering of time-varying sparse signals. Of the existing algorithms, fewer still provide strong performance guarantees. This paper examines two algorithms f…
Paper proves noise-tolerant SSC using greedy methods under coherence conditions.
problem Proving noise-tolerant SSC using greedy methods under coherence conditions.
method Derives coherence-based sufficient conditions for correct neighbor identification using MP/OMP in the presence of bounded noise.
result MP/OMP succeed in identifying correct neighbors under certain noise levels, leading to higher clustering accuracy.
Unions of subspaces provide a powerful generalization to linear subspace models for collections of high-dimensional data. To learn a union of subspaces from a collection of data, sets of signals in the collection that belong to the same subspace must be identified in order to obtain accurate estimates of the subspace s…
This paper studies the problem of optimally allocating a cash injection into a financial system in distress. Given a one-period borrower-lender network in which all debts are due at the same time and have the same seniority, we address the problem of allocating a fixed amount of cash among the nodes to minimize the wei…