26 papers · ranked by Valyu relevance
Kun Yu, Weidong Xie, Linjie Wang, Wei Li
Background Finding significant genes or proteins from gene chip data for disease diagnosis and drug development is an important task. However, the challenge comes from the curse of the data dimension. It is of great significance to use machine learning methods to find important features from the data and build an…
Qianru Liu, Rui Wang, Yuesheng Xu, Mingsong Yan
We consider a regularization problem whose objective function consists of a convex fidelity term and a regularization term determined by the `1 norm composed with a linear transform. Empirical results show that the regularization with the `1 norm can promote sparsity of a regularized solution. It is the goal of this…
Yao Li, Mo Li, Lei Zhang, Mahdi Roozbeh
Evolutionary polynomial regression (EPR) is a data mining tool that has been widely used in solving various geotechnical engineering problems. The fitness function is the core of EPR. However, overfitting may still occur in EPR, and this issue may cause the testing dataset not to perform effectively. Improvement of the…
Wang, Jingyuan, Ji, Jiahao
This article serves as the regression analysis lecture notes in the Intelligent Computing course cluster (including the courses of Artificial Intelligence, Data Mining, Machine Learning, and Pattern Recognition) at the School of Computer Science and Engineering, Beihang University. It aims to provide students – who are…
Anqi Fu, Vicki Trier Taasti, Masoud Zarepisheh
Approach: We formulated the proton treatment planning problem as a convex optimization problem with a cost function consisting of a dosimetric plan quality term plus a weighted l1 regularization term. We iteratively solved this problem and adaptively updated the regularization weights to promote the sparsity of both…
Egill A Fridgeirsson, Ross Williams, Peter Rijnbeek, Marc A Suchard + 1 more
A recent review of the use of clinical prediction models finds that in recent years 67% of studies use some kind of regression analysis.1 One common issue when developing clinical prediction models is the susceptibility to overfitting. Overfitting occurs when a model is overly complex such that it near perfectly fits…
Yuyuan Ouyang, Kyle Yates
The sparse linear reconstruction problem is a core problem in signal processing which aims to recover sparse solutions to linear systems. The original problem regularized by the total number of nonzero components (also know as L0 regularization) is well-known to be NP-hard. The relaxation of the L0 regularization by…
Anastasia Sholokhova, Dmitriy Matyushin, Mikhail Shashkov
Ionic liquids, i.e., organic salts with a low melting point, can be used as gas chromatographic liquid stationary phases. These stationary phases have some advantages such as peculiar selectivity, high polarity, and thermostability. Many previous works are devoted to such stationary phases. However, there are still no…
Edric Tam, David B. Dunson
We introduce Fiedler regularization, a novel approach for regularizing neural networks that utilizes spectral/graphical information. Existing regularization methods often focus on penalizing weights in a global/uniform manner that ignores the connectivity structure of the neural network. We propose to use the Fiedler…
Ryosuke Kasai, Hideki Otsuka, Mrinal Mandal
This study proposes a novel image reconstruction algorithm for nuclear medicine imaging based on the maximum likelihood expectation maximization (MLEM) framework with dynamic ElasticNet regularization. Whereas conventional the L1 and L2 regularization methods involve trade-offs between noise suppression and structural…
Esmaeil Memarzadeh, Dionisio Bernal, Martin Dalgaard Ulriksen
The idea of exploiting sparseness in under-determined damage characterization problems is not new, and regularizations techniques that tend to promote sparseness, such as L1 norm minimization, have been investigated in the last ten years or so. Although various claims of merit have been made, two interconnected issues…
Jake Crawford, Maria Chikina, Casey S. Greene
Most models can be fit to data using various optimization approaches. While model choice is frequently reported in machine-learning-based research, optimizers are not often noted. We applied two different implementations of LASSO logistic regression implemented in Python’s scikit-learn package, using two different…
Xin Wang, Xu Ren, Haoquan Wang
Significance Photoacoustic tomography (PAT) is an emerging biomedical imaging technology that offers high contrast and high resolution, showing great potential for applications in medical imaging. However, existing regularization methods often lead to instability and artifacts in the reconstruction due to imbalanced…
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…
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…
Griffin S. Hampton, Ryan Neff, Zezheng Song, Mustapha Bouhrara + 2 more
Myelin water fraction (MWF) mapping in the central nervous system is a topic of intense research activity. One framework for this requires parameter estimation from a decaying biexponential signal. However, this is often an ill-posed nonlinear problem resulting in unreliable parameter estimates. For linear…
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…
Guanghui Zhang, Xinran Wang, Steven J. Luck
Regularization has been extensively used in multivariate pattern classification (MVPA; decoding) of EEG data to mitigate the risk of overfitting. N-fold cross-validation is also used to mitigate this risk, and it is often combined with averaging across trials to improve the signal-to-noise ratio. However, the impact of…
Xinghua Liu, Ming Cao
—The paper proposes a novel regularization procedure for machine learning. The proposed high-order regularization (HR) provides new insight into regularization, which is widely used to train a neural network that can be utilized to approximate the action-value function in general reinforcement learning problems. The…
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…
Søren A. Fuglsang, Kristoffer H. Madsen, Oula Puonti, Hartwig R. Siebner + 1 more
Regression is a principal tool for relating brain responses to stimuli or tasks in computational neuroscience. This often involves fitting linear models with predictors that can be divided into groups, such as distinct stimulus feature subsets in encoding models or features of different neural response channels in…
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…
Lea Duncker, Kiersten M. Ruda, Greg D. Field, Jonathan W. Pillow
An important problem in systems neuroscience is to characterize how a neuron integrates sensory inputs across space and time. The linear receptive field provides a mathematical characterization of this weighting function, and is commonly used to quantify neural response properties and classify cell types. However…
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…
Kan Hatakeyama-Sato, Seigo Watanabe, Naoki Yamane, Yasuhiko Igarashi + 1 more
Materials informatics and cheminformatics struggle with data scarcity, hindering the extraction of significant relationships between structures and properties. The "Ugly Duckling" theorem, suggesting the difficulty of data processing without assumptions or prior knowledge, exacerbates this problem. Current…