22 papers · ranked by Valyu relevance
Hongfu Guo, Wencheng Zou, Zeyu Zhang, Shuishan Zhang + 2 more
MANIFOLD REGULARIZATION CLASSIFICATION MODEL BASED ON IMPROVED DIFFUSION MAP Hongfu Guo Leceister Institution Dalian University of Technology hg203@student.le.ac.uk Shuishan Zhang Dalian Leceister Institution Dalian University of Technology sz252@student.le.ac.uk Wencheng Zou Dalian Leceister Institution Dalian…
Muhammad Zafran Muhammad Zaly Shah, Anazida Zainal, Fuad A. Ghaleb, Abdulrahman Al-Qarafi + 2 more
Data streaming applications such as the Internet of Things (IoT) require processing or predicting from sequential data from various sensors. However, most of the data are unlabeled, making applying fully supervised learning algorithms impossible. The online manifold regularization approach allows sequential learning…
Ding Li, Scott Dick
Graph-based algorithms are known to be effective approaches to semi-supervised learning. However, there has been relatively little work on extending these algorithms to the multi-label classification case. We derive an extension of the Manifold Regularization algorithm to multi-label classification, which is…
Madiha Javeed, Mohammad Shorfuzzaman, Nawal Alsufyani, Samia Allaoua Chelloug + 3 more
'Samia Allaoua Chelloug' 'Ahmad Jalal' 'Jeongmin Park' 'Yilun Shang'] Human locomotion is an imperative topic to be conversed among researchers. Predicting the human motion using multiple techniques and algorithms has always been a motivating subject matter. For this, different methods have shown the ability of…
Alin Dondera, Anuj Singh, Hadi Jamali‐Rad
Masked Autoencoders (MAEs) are an important divide in self-supervised learning (SSL) due to their independence from augmentation techniques for generating positive (and/or negative) pairs as in contrastive frameworks. Their masking and reconstruction strategy also nicely aligns with SSL approaches in natural language…
Youyu Liu, Baozhu Zou, Jiao Xu, Siyang Yang + 2 more
'Anastasios Doulamis'] A point cloud obtained by stereo matching algorithm or three-dimensional (3D) scanner generally contains much complex noise, which will affect the accuracy of subsequent surface reconstruction or visualization processing. To eliminate the complex noise, a new regularization algorithm for…
Erica L. Busch, E. Chandra Fincke, Guillaume Lajoie, Smita Krishnaswamy + 1 more
Brain-computer interfaces (BCIs) promise to restore and enhance a wide range of human capabilities. However, a barrier to the adoption of BCIs is how long it can take users to learn to control them. We hypothesized that human BCI learning could be accelerated by leveraging the naturally occurring geometric structure of…
Andrew Cheng, Melanie Weber
Manifold Authors: ['Andrew Cheng' 'Melanie Weber'] Matrix-valued optimization tasks, including those involving symmetric positive definite (SPD) matrices, arise in a wide range of applications in machine learning, data science and statistics. Classically, such problems are solved via constrained Euclidean optimization…
Liangchen Liu, Juncai He, Richard Tzong‐Han Tsai
In this paper, we study linear regression applied to data structured on a manifold. We assume that the data manifold is smooth and is embedded in a Euclidean space, and our objective is to reveal the impact of the data manifold's extrinsic geometry on the regression. Specifically, we analyze the impact of the…
Zhigang Yao, Jiaji Su
With data growing in scale and complexity, traditional linear dimension reduction techniques are becoming inadequate in some settings. Manifold fitting offers an important alternative by capturing low-dimensional latent geometric structures within high-dimensional spaces. This capability allows it to support downstream…
Jisui Huang, Ke Chen, Andreas Alpers, Na Lei
Existing level set models employ regularization based only on gradient information, 1D curvature or 2D curvature. For 3D image segmentation, however, an appropriate curvature-based regularization should involve a well-defined 3D curvature energy. This is the first paper to introduce a regularization energy that…
Authors not listed
Second-order Møller-Plesset perturbation theory is well-known as a computationally inexpensive approach to the elec- tron correlation problem that is size-consistent, but fails to be regular. On the other hand, the less well-known many- body version of Brillouin-Wigner (BW) perturbation theory has the reverse…
Chun Kit Jeffery Hou, Kamran Behdinan
Surrogate modeling has been popularized as an alternative to full-scale models in complex engineering processes such as manufacturing and computer-assisted engineering. The modeling demand exponentially increases with complexity and number of system parameters, which consequently requires higher-dimensional engineering…
Dhruv Kohli, Johannes S. Nieuwenhuis, Alexander Cloninger, Gal Mishne + 1 more
With the ubiquity of high-dimensional datasets in various biological fields, identifying low-dimensional topological manifolds within such datasets may reveal principles connecting latent variables to measurable instances in the world. The reliable discovery of such manifold structure in high-dimensional datasets can…
Dhruv Kohli, Johannes S. Nieuwenhuis, Katja Zegwaard, Alexander Cloninger + 2 more
With the ubiquity of high-dimensional datasets in various biological fields, identifying low-dimensional topological manifolds within such datasets may reveal principles connecting latent variables to measurable instances in the world. The reliable discovery of such manifold structure in high-dimensional datasets can…
Serena Hughes, Timothy Hamilton, Tom Kolokotrones, Eric J. Deeds
Manifold learning builds on the “manifold hypothesis,” which posits that data in high-dimensional datasets are drawn from lower-dimensional manifolds. Current tools generate global embeddings of data, rather than the local maps used to define manifolds mathematically. These tools also cannot assess whether the manifold…
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…
Stefano Battaglia, Lina Fransén, Ignacio Fdez. Galván, Roland Lindh
In this work we present a new approach to fix the intruder state problem (ISP) in CASPT2 based on σp regularization. The resulting \$σ^p\$-CASPT2 method is compared to previous techniques, namely the real and imaginary level shifts, on a theoretical basis and by performing a series of systematic calculations. The…
Keisuke Ozawa
Statistically weighted principal component analysis (wPCA) is widely used to reduce the noise of scanning transmission electron microscopy-energy-dispersive X-ray (STEM-EDX) spectroscopy data. It is beneficial to retain the spatial resolution of observation in each step of the analysis, but the direct application of…
Alexander Smith, Spencer Runde, Alex Chew, Atharva Kelkar + 3 more
Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex datasets that capture the 3D spatial positions, dynamics, and interactions of thousands…
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…