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Found 18 papers, 7 papers with code
High-Dimensional False Discovery Rate Control for Dependent Variables
In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providing guarantees even in high-dimensional settings where the number of variables surpasses the number of samples.
FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking
In high-dimensional data analysis, such as financial index tracking or biomedical applications, it is crucial to select the few relevant variables while maintaining control over the false discovery rate (FDR).
False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening
Unfortunately, well-established estimators, such as the graphical lasso or neighborhood selection, are known to be susceptible to a high prevalence of false edge detections.
Sparse PCA with False Discovery Rate Controlled Variable Selection
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension.
Fast and Robust Sparsity-Aware Block Diagonal Representation
The block diagonal structure of an affinity matrix is a commonly desired property in cluster analysis because it represents clusters of feature vectors by non-zero coefficients that are concentrated in blocks.
Accelerated Sample-Accurate R-Peak Detectors Based on Visibility Graphs
Further acceleration is obtained by adopting the computationally efficient horizontal visibility graph, which has not yet been used for R-peak detection.
Emergency Response Person Localization and Vital Sign Estimation Using a Semi-Autonomous Robot Mounted SFCW Radar
The large number and scale of natural and man-made disasters have led to an urgent demand for technologies that enhance the safety and efficiency of search and rescue teams.
Shuffled Multi-Channel Sparse Signal Recovery
Assuming that we have a sensing matrix for the underlying signals, we show that the problem is equivalent to a structured unlabeled sensing problem, and establish sufficient conditions for unique recovery.
Robust and Efficient Aggregation for Distributed Learning
This is followed by an aggregation step, which traditionally takes the form of a (weighted) average.
The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control
The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables.
Robust Regularized Locality Preserving Indexing for Fiedler Vector Estimation
The Fiedler vector of a connected graph is the eigenvector associated with the algebraic connectivity of the graph Laplacian and it provides substantial information to learn the latent structure of a graph.
Sparsity-aware Robust Community Detection(SPARCODE)
We compare the performance to popular graph and cluster-based community detection approaches on a variety of benchmark network and cluster analysis data sets.
Real Elliptically Skewed Distributions and Their Application to Robust Cluster Analysis
This article proposes a new class of Real Elliptically Skewed (RESK) distributions and associated clustering algorithms that allow for integrating robustness and skewness into a single unified cluster analysis framework.
Robust M-Estimation Based Bayesian Cluster Enumeration for Real Elliptically Symmetric Distributions
Robustly determining the optimal number of clusters in a data set is an essential factor in a wide range of applications.
Robust Bayesian Cluster Enumeration Based on the $t$ Distribution
Hence, we propose a two-step cluster enumeration algorithm that uses the expectation maximization algorithm to partition the data and estimate cluster parameters prior to the calculation of one of the robust criteria.
Bayesian Cluster Enumeration Criterion for Unsupervised Learning
We derive a new Bayesian Information Criterion (BIC) by formulating the problem of estimating the number of clusters in an observed data set as maximization of the posterior probability of the candidate models.
Gravitational Clustering: A Simple, Robust and Adaptive Approach for Distributed Networks
The cluster enumeration exploits the fact that the highest attraction on the mobile mass units is exerted by regions with a high density of feature vectors, i. e., gravitational clusters.
Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals
Wearable devices that acquire photoplethysmographic (PPG) signals are becoming increasingly popular to monitor the heart rate during physical exercise.
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Heart rate estimation
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