New L0 norm added to TDA for market analysis.
problem Improving TDA tools for market prediction.
method Defined and applied L0 norm in TDA for four markets.
result Enhanced TDA tools for market analysis.
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22 results for “l0-norm”
Regularization vs. Relaxation: A conic optimization perspective of statistical variable selectioncs.LG
Variable selection is a fundamental task in statistical data analysis. Sparsity-inducing regularization methods are a popular class of methods that simultaneously perform variable selection and model estimation. The central problem is a quadratic optimization problem with an l0-norm penalty. Exactly enforcing the l0-no…
Paper proposes methods to train RBF networks under faults.
problem Training RBF networks with fault tolerance.
method Two novel ADMM-based algorithms using MCP and l0-norm.
result Both methods globally converge to a unique limit point.
New algorithm targets nonsmooth constraints for robust data interpolation and denoising.
problem Robust data interpolation and denoising with large outliers and varying amplitudes.
method Flexible algorithmic framework targeting nonsmooth level-set constraints (L1, Linf, L0 norms).
result Improved robustness to large outliers and significant amplitude variations in seismic data.
New group-sparse SVD models improve biclustering of gene expression data.
problem Identifying block patterns with similar expressions in high-dimensional gene expression data.
method Proposed GL1-SVD, GL0-SVD, OGL1-SVD, and OGL0-SVD models with group Lasso and L0-norm penalties, using alternating iterative strategies and ADMM.
result Effective in identifying biologically interpretable gene modules with gene prior group knowledge.
Improved sample efficiency in learning sparse Ising models.
problem Learning the graph of a sparse Ising model with limited samples.
method Combining L0 and L2 norms to induce sparsity and model non-zero coefficients.
result Improved sample complexity, achieving new state-of-the-art recovery guarantees.
We propose an algorithm for the non-negative factorization of an occurrence tensor built from heterogeneous networks. We use l0 norm to model sparse errors over discrete values (occurrences), and use decomposed factors to model the embedded groups of nodes. An efficient splitting method is developed to optimize the non…
This paper prunes deep neural networks by grouping channels and using a bounded L1-L0 norm.
problem Improving network efficiency by reducing the number of parameters in deep neural networks.
method Group-wise pruning with a bounded L1-L0 norm regularizer.
result Significant reduction in model size with minimal loss in accuracy.
Structured pruning method reduces RNN sizes and speeds up inference.
problem Large RNN models are hard to deploy on edge devices.
method Structured pruning through neuron selection, minimizing L0 norm of weight matrix.
result Nearly 20x speedup achieved without performance loss.
Signal recovery is one of the key techniques of Compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one domain to formulate the L0 norm optimization. Recent investigation shows that some signals are sparse in multiple domains.…
The paper develops a classification method using penalties on feature selection for high-dimensional data.
problem High-dimensional binary classification with many irrelevant features.
method Empirical risk minimization with l0-penalization for feature selection.
result The method achieves a sparse solution close to true sparsity with high probability and converges to low misclassification risk.
Method introduces topological regularization using information filtering networks.
problem Sparse probabilistic modeling and multicollinear regression.
method Topological regularization via information filtering network.
result Direct application to -norm regularized problems.
Scoring systems are classification models that only require users to add, subtract and multiply a few meaningful numbers to make a prediction. These models are often used because they are practical and interpretable. In this paper, we introduce an off-the-shelf tool to create scoring systems that both accurate and inte…
Reweighted l1-algorithms have attracted a lot of attention in the field of applied mathematics. A unified framework of such algorithms has been recently proposed by Zhao and Li. In this paper we construct a few new examples of reweighted l1-methods. These functions are certain concave approximations of the l0-norm func…
Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensing of multi-channel EEG signals. Methods: An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low r…
Paper proposes a new sparse group k-max regularization for sparsity constraints.
problem Linear inverse problems with sparsity constraints are NP-hard.
method Sparse group k-max regularization, iterative soft thresholding algorithm.
result Approximates l0 norm more closely and enhances group-wise and in-group sparsity.
Learning the "blocking" structure is a central challenge for high dimensional data (e.g., gene expression data). Recently, a sparse singular value decomposition (SVD) has been used as a biclustering tool to achieve this goal. However, this model ignores the structural information between variables (e.g., gene interacti…
New method improves signal estimation by convexifying -norm constraints.
problem Signal estimation with sparsity and smoothness priors.
method Iterative convex conic quadratic relaxations exploiting -norm and smoothness terms.
result Significantly better estimators than -norm approaches and interpretable parameters.
A stealthy framework injects faults into DNNs to misclassify images without affecting overall accuracy.
problem Vulnerability of deep neural networks to misclassification attacks.
method Fault sneaking attack using ADMM optimization with constraints on maintaining model accuracy and minimizing parameter modifications.
result The framework can inject multiple sneaking faults into DNNs without reducing overall accuracy.
Proposes sparse QSVM for better generalization and interpretability.
problem Overfitting and difficulty in interpreting full quadratic classifiers.
method Enforces -norm constraint to promote sparsity and develops a penalty decomposition algorithm.
result The proposed model enhances generalization and produces sparse solutions.
New method quantifies market shocks and their effects.
problem Quantifying the impact and response to market shocks.
method Sparse probabilistic elliptical model with -norm regularization.
result Shock patterns are related to network structure and sector diversification.
Sparse modeling improves portfolio optimization by reducing errors in complex market systems.
problem Errors in multivariate modeling of markets and economy.
method L0-norm sparse elliptical modeling to reduce oversimplification, and study likelihood in- and out-of-sample for different parameter lengths.
result Sparse models lead to better portfolio performance, higher out-of-sample likelihood, and lower volatility.