Search results for "sparsity"
showing 10 items of 14 documents
An Extension of the DgLARS Method to High-Dimensional Relative Risk Regression Models
2020
In recent years, clinical studies, where patients are routinely screened for many genomic features, are becoming more common. The general aim of such studies is to find genomic signatures useful for treatment decisions and the development of new treatments. However, genomic data are typically noisy and high dimensional, not rarely outstripping the number of patients included in the study. For this reason, sparse estimators are usually used in the study of high-dimensional survival data. In this paper, we propose an extension of the differential geometric least angle regression method to high-dimensional relative risk regression models.
Event Reconstruction
2014
Event reconstruction is one of the most important step in digital forensic investigations. It allows investigators to have a clear view of the events that have occurred over time. Event reconstruction is a complex task which requires exploration of a large amount of events due to the pervasiveness of new technologies nowadays. Any evidence produced at the end of the investigative process must also meet the requirements of the courts, such as reproducibility, verifiability, validation, etc. After defining the most important concepts of event reconstruction, a survey of the challenges of this field and solutions proposed so far is given in this chapter. Irish Research Council Science Foundati…
Sparsity-aware narrowband interference mitigation and subcarriers selection in OFDM-based cognitive radio networks
2016
In this paper, the performance of an orthogonal frequency division multiplexing overlay cognitive radio network with subcarrier selection schemes is investigated. We propose three subcarrier selection techniques that reduce the level of interference at the primary base station based on collected channel state information from the different network nodes. Approximated outage probability expressions are also derived and verified by simulations for the different studied techniques. In addition, we propose and investigate a new approach for asynchronous narrowband interference (NBI) estimation and mitigation in cognitive radio networks. The proposed approach does not require prior knowledge of …
A Sparsity-Aware Approach for NBI Estimation and Mitigation in Large Cognitive Radio Networks
2016
Underlay cognitive networks should follow strict interference thresholds to operate in parallel with primary networks. This constraint limits their transmission power and eventually the coverage area. Therefore, in this paper, we first design a new approach for asynchronous narrow-band interference (NBI) estimation and mitigation in orthogonal frequency-division multiplexing cognitive radio networks that does not require prior knowledge of the NBI characteristics. Our proposed approach allows the primary user to exploit the sparsity of the secondary users' interference signal to recover it and cancel it based on sparse signal recovery theory. We also propose two subcarrier selection schemes…
Secondary users selection and sparse narrow-band interference mitigation in cognitive radio networks
2018
Spectrum scarcity is a critical problem that may reduce the effectiveness of wireless technologies and services. To address this problem, different spectrum management techniques have been proposed in the literature such as overlay cognitive radio (CR) where the unlicensed users can share the same spectrum with the licensed users. The main challenges in overlay CR networks are the identification and detection of the Primary User (PU) signals in a multi-source narrow-band interference (NBI) scenario. Therefore, in this paper, we investigate the performance of an orthogonal frequency division multiplexing (OFDM) overlay CR network with Secondary Users (SUs) and subcarriers selection schemes. …
Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition with Spatial Sparsity Constraint
2022
Tucker decomposition can provide an intuitive summary to understand brain function by decomposing multi-subject fMRI data into a core tensor and multiple factor matrices, and was mostly used to extract functional connectivity patterns across time/subjects using orthogonality constraints. However, these algorithms are unsuitable for extracting common spatial and temporal patterns across subjects due to distinct characteristics such as high-level noise. Motivated by a successful application of Tucker decomposition to image denoising and the intrinsic sparsity of spatial activations in fMRI, we propose a low-rank Tucker-2 model with spatial sparsity constraint to analyze multi-subject fMRI dat…
Sparse relative risk regression models
2020
Summary Clinical studies where patients are routinely screened for many genomic features are becoming more routine. In principle, this holds the promise of being able to find genomic signatures for a particular disease. In particular, cancer survival is thought to be closely linked to the genomic constitution of the tumor. Discovering such signatures will be useful in the diagnosis of the patient, may be used for treatment decisions and, perhaps, even the development of new treatments. However, genomic data are typically noisy and high-dimensional, not rarely outstripping the number of patients included in the study. Regularized survival models have been proposed to deal with such scenarios…
The conditional censored graphical lasso estimator
2020
© 2020, Springer Science+Business Media, LLC, part of Springer Nature. In many applied fields, such as genomics, different types of data are collected on the same system, and it is not uncommon that some of these datasets are subject to censoring as a result of the measurement technologies used, such as data generated by polymerase chain reactions and flow cytometer. When the overall objective is that of network inference, at possibly different levels of a system, information coming from different sources and/or different steps of the analysis can be integrated into one model with the use of conditional graphical models. In this paper, we develop a doubly penalized inferential procedure for…
A differential-geometric approach to generalized linear models with grouped predictors
2016
We propose an extension of the differential-geometric least angle regression method to perform sparse group inference in a generalized linear model. An efficient algorithm is proposed to compute the solution curve. The proposed group differential-geometric least angle regression method has important properties that distinguish it from the group lasso. First, its solution curve is based on the invariance properties of a generalized linear model. Second, it adds groups of variables based on a group equiangularity condition, which is shown to be related to score statistics. An adaptive version, which includes weights based on the Kullback-Leibler divergence, improves its variable selection fea…
cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values
2023
Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse co…