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Found 17 papers, 9 papers with code
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection
We introduce a new anomaly detection model that unifies the OC-SVM and DL residual functions into a single composite objective, subsequently solved through K-SVD-type iterative algorithms.
Learning Explicitly Conditioned Sparsifying Transforms
Sparsifying transforms became in the last decades widely known tools for finding structured sparse representations of signals in certain transform domains.
Nodal Hydraulic Head Estimation through Unscented Kalman Filter for Data-driven Leak Localization in Water Networks
In this paper, we present a nodal hydraulic head estimation methodology for water distribution networks (WDN) based on an Unscented Kalman Filter (UKF) scheme with application to leak localization.
Learning Dictionaries from Physical-Based Interpolation for Water Network Leak Localization
This article presents a leak localization methodology based on state estimation and learning.
Unsupervised Abnormal Traffic Detection through Topological Flow Analysis
Cyberthreats are a permanent concern in our modern technological world.
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection
In this paper we use a particular DL formulation that seeks uniform sparse representations model to detect the underlying subspace of the majority of samples in a dataset, using a K-SVD-type algorithm.
Data-driven Leak Localization in Water Distribution Networks via Dictionary Learning and Graph-based Interpolation
In this paper, we propose a data-driven leak localization method for water distribution networks (WDNs) which combines two complementary approaches: graph-based interpolation and dictionary classification.
Complexity of Inexact Proximal Point Algorithm for minimizing convex functions with Holderian Growth
Several decades ago the Proximal Point Algorithm (PPA) started to gain a long-lasting attraction for both abstract operator theory and numerical optimization communities.
Efficient and Parallel Separable Dictionary Learning
Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images.
Stochastic Proximal Gradient Algorithm with Minibatches. Application to Large Scale Learning Models
Stochastic optimization lies at the core of most statistical learning models.
Fault Handling in Large Water Networks with Online Dictionary Learning
Fault detection and isolation in water distribution networks is an active topic due to its model's mathematical complexity and increased data availability through sensor placement.
Unsupervised Dictionary Learning for Anomaly Detection
We investigate the possibilities of employing dictionary learning to address the requirements of most anomaly detection applications, such as absence of supervision, online formulations, low false positive rates.
Stochastic proximal splitting algorithm for composite minimization
In the large-scale or noisy contexts, when only stochastic information on the smooth part of the objective function is available, the extension of proximal gradient schemes to stochastic oracles is based on proximal tractability of the nonsmooth component and it has been deeply analyzed in the literature.
Community-Level Anomaly Detection for Anti-Money Laundering
Anomaly detection in networks often boils down to identifying an underlying graph structure on which the abnormal occurrence rests on.
Quick survey of graph-based fraud detection methods
In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles.
Overcomplete Dictionary Learning with Jacobi Atom Updates
Dictionary learning for sparse representations is traditionally approached with sequential atom updates, in which an optimized atom is used immediately for the optimization of the next atoms.
Efficient GPU Implementation for Single Block Orthogonal Dictionary Learning
Dictionary training for sparse representations involves dealing with large chunks of data and complex algorithms that determine time consuming implementations.
