27 papers · ranked by Valyu relevance
Hussein Hazimeh, Rahul Mazumder, Tim Nonet
We present L0Learn: an open-source package for sparse linear regression and classification using ℓ 0 regularization. L0Learn implements scalable, approximate algorithms, based on coordinate descent and local combinatorial optimization. The package is built using C++ and has user-friendly R and Python interfaces.…
William Herlands, Maria De‐Arteaga, Daniel B. Neill, Artur Dubrawski
We compute approximate solutions to L0 regularized linear regression using L1 regularization, also known as the Lasso, as an initialization step. Our algorithm, the Lass0 ("Lass-zero"), uses a computationally efficient stepwise search to determine a locally optimal L0 solution given any L1 regularization solution. We…
Sheraz Ahmed
— We consider complexity of Deep Neural Networks (DNNs) and their associated massive over-parameterization. Such over-parametrization may entail susceptibility to adversarial attacks, loss of interpretability and adverse Size, Weight and Power - Cost (SWaP-C) considerations. We ask if there are methodical ways…
Zhenqiu Liu, Gang Li
Variable (feature, gene, model, which we use interchangeably) selections for regression with high-dimensional BIGDATA have found many applications in bioinformatics, computational biology, image processing, and engineering. One appealing approach is the L0 regularized regression which penalizes the number of nonzero…
Anni S. Halkola, Kaisa Joki, Tuomas Mirtti, Marko M. Mäkelä + 2 more
In many real-world applications, such as those based on patient electronic health records, prognostic prediction of patient survival is based on heterogeneous sets of clinical laboratory measurements. To address the trade-off between the predictive accuracy of a prognostic model and the costs related to its clinical…
Kory D. Johnson, Dongyu Lin, Lyle Ungar, Dean P. Foster + 1 more
'Robert A. Stine'] There has been an explosion of interest in using l1-regularization in place of l0-regularization for feature selection. We present theoretical results showing that while l1-penalized linear regression never outperforms l0-regularization by more than a constant factor, in some cases using an l1…
Nurdan Ayse Saran, Fatih Nar, Charles Elkan
This study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community. Our method achieves training times an order of magnitude faster than traditional logistic regression by employing a novel Soft-Plus…
Sarah Friedrich, Andreas Groll, Katja Ickstadt, Thomas Kneib + 3 more
methods and their applications Authors: ['Sarah Friedrich' 'Andreas Groll' 'Katja Ickstadt' 'Thomas Kneib' 'Markus Pauly' 'Jörg Rahnenführer' 'Tim Friede'] A range of regularization approaches have been proposed in the data sciences to overcome overfitting, to exploit sparsity or to improve prediction. Using a broad…
Bingyuan Wang, Wenbo Wan, Yihan Wang, Wenjuan Ma + 5 more
'Jiao Li' 'Zhongxing Zhou' 'Huijuan Zhao' 'Feng Gao'] Background In diffuse optical tomography (DOT), the image reconstruction is often an ill-posed inverse problem, which is even more severe for breast DOT since there are considerably increasing unknowns to reconstruct with regard to the achievable number of…
Xueyan Liu, Limei Zhang, Yining Zhang, Lishan Qiao
The recently emerging technique of sparse reconstruction has received much attention in the field of photoacoustic imaging (PAI). Compressed sensing (CS) has large potential in efficiently reconstructing high-quality PAI images with sparse sampling signal. In this article, we propose a CS-based error-tolerant…
Jianwei Liu, Shuang Cheng Li, Xionglin Luo
Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data. L2 regularization has been commonly used. If the training dataset contains many noise variables, L1 regularization SVM will provide a…
Hussein Hazimeh, Rahul Mazumder
The L0-regularized least squares problem (aka best subsets) is central to sparse statistical learning and has attracted significant attention across the wider statistics, machine learning, and optimization communities. Recent work has shown that modern mixed integer optimization (MIO) solvers can be used to address…
Ziran Wei, Jianlin Zhang, Zhiyong Xu, Yongmei Huang + 2 more
'Xiangsuo Fan'] In the reconstruction of sparse signals in compressed sensing, the reconstruction algorithm is required to reconstruct the sparsest form of signal. In order to minimize the objective function, minimal norm algorithm and greedy pursuit algorithm are most commonly used. The minimum L1 norm algorithm has…
Ronghuo Dai, Jun Yang
Impedance inversion of post-stack seismic data is a key technology in reservoir prediction and characterization. Compared to the common used single-trace impedance inversion, multi-trace impedance simultaneous inversion has many advantages. For example, it can take lateral regularization constraint to improve the…
Holger Mohr, Hannes Ruge
In certain modeling approaches, activation analyses of task-based fMRI data can involve a relatively large number of predictors. For example, in the encoding model approach, complex stimuli are represented in a high-dimensional feature space, resulting in design matrices with many predictors. Similarly, single-trial…
Gabriel F. Dorlhiac, Clyde Fare, Jasper J. van Thor, Timothée Poisot
Ultrafast spectroscopy offers temporal resolution for probing processes in the femto- and picosecond regimes. This has allowed for investigation of energy and charge transfer in numerous photoactive compounds and complexes. However, analysis of the resultant data can be complicated, particularly in more complex…
Georg Hahn, Sharon M. Lutz, Nilanjana Laha, Michael Cho + 2 more
High dimensional linear regression problems are often fitted using LASSO-type approaches. Although the LASSO objective function is convex, it is not differentiable everywhere, making the use of gradient descent methods for minimization not straightforward. To avoid this technical issue, we apply Nesterov smoothing to…
Adeleke Maradesa, Baptiste Py, Ting Hei Wan, Mohammed B. Effat + 1 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique used widely in electrochemistry. Obtaining EIS data is simple when modern electrochemical workstations are used; however, analyzing EIS spectra is still a considerable quandary. The distribution of relaxation times (DRT) has emerged as a…
Sanjar Adilov
Machine learning models for molecular-property prediction typically work with molecular representations in the form of fingerprints, descriptors, or graphs. In case of fingerprints and descriptors, molecular representations usually comprise thousands of features, which causes the curse of dimensionality for many…
Marijn van Vliet, Riitta Salmelin
Linear machine learning models “learn” a data transformation by being exposed to examples of input with the desired output, forming the basis for a variety of powerful techniques for analyzing neuroimaging data. However, their ability to learn the desired transformation is limited by the quality and size of the example…
Giorgio L. Manenti, Sepideh Khoneiveh, Jan Gläscher
Theory of Mind (ToM) refers to the ability to infer another agent’s latent mental states, such as intentions, beliefs, and strategies, to predict their behavior. A core feature of ToM is its recursive structure: individuals reason not only about what others think, but also about what others think about them. Existing…
Baptiste Py, Francesco Ciucci
The distribution of relaxation times (DRT) has emerged as a promising method for analyzing electrochemical impedance spectroscopy (EIS) data. The standard approach for reconstructing the DRT from measured impedances consists of regularized regression, which usually leverages the Euclidean norm. In this work, we show…
Sarah C. Bickers, Samir Benlekbir, John L. Rubinstein, Voula Kanelis
ATP binding cassette (ABC) proteins typically function in active transport of solutes across membranes. The ABC core structure is comprised of two transmembrane domains (TMD1 and TMD2) and two cytosolic nucleotide binding domains (NBD1 and NBD2). Some members of the C-subfamily of ABC (ABCC) proteins, including human…
Marta Karas, Damian Brzyski, Mario Dzemidzic, Joaquin Goni + 3 more
A challenging problem arising in brain imaging research is principled incorporation of information from different imaging modalities. Frequently each modality is analyzed separately using, for instance, dimensionality reduction techniques which result in a loss of mutual information. We propose a novel regularization…
Authors not listed
Electrochemical impedance spectroscopy (EIS) coupled with distribution of relaxation times (DRT) analysis is a robust framework for characterizing electrochemical systems. However, DRT deconvolution is often plagued by spurious peaks, hindering accurate process identification and quantitative parameter estimation. To…
Denis Tikhonov
Here, we present a new approach for obtaining radial distribution functions (RDF) from the electron diffraction data using a regularized weighted sine least-squares spectral analysis (rwsLSSA). It allows for explicitly transferring the measured experimental uncertainties in the reduced molecular scattering function to…
Charles Eads
This report describes and illustrates a set of automatable multicomponent exponential relaxation analysis protocols that are model-agnostic and suited to extracting information under circumstances when little prior knowledge about the underlying system is used. Methods are illustrated and mathematical and physical…