16 papers · ranked by Valyu relevance
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…
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…
Junmin Liu, Chunxia Zhang, Jiangshe Zhang, Huirong Li + 1 more
Background Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of…
Ke Wang, Fengqing Qin
The novel fault diagnosis method of gearbox based on Fourier Bessel series expansion-based empirical wavelet transform (FBEWT) and manifold regularization extreme learning machine (MRELM) is proposed to obtain excellent fault diagnosis results of gearbox in this paper. A new feature extraction strategy based on Fourier…
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…
René Ciak, Melanie Melching, Otmar Scherzer
We present an approach for variational regularization of inverse and imaging problems for recovering functions with values in a set of vectors. We introduce regularization functionals, which are derivative-free double integrals of such functions. These regularization functionals are motivated from double integrals…
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…
Darius Erös, Michael Kunzinger, Argam Ohanyan, Alessio Vardabasso
Sectional curvature bounds are of central importance in the study of Riemannian manifolds, both in smooth differential geometry and in the generalized synthetic setting of Alexandrov spaces. Riemannian metrics along with metric spaces of bounded sectional curvature enjoy a variety of, oftentimes rigid, geometric…
Weifeng Liu, Yang Li, Xu Lin, Dacheng Tao + 2 more
'Kewei Chen'] Co-training is a major multi-view learning paradigm that alternately trains two classifiers on two distinct views and maximizes the mutual agreement on the two-view unlabeled data. Traditional co-training algorithms usually train a learner on each view separately and then force the learners to be…
Zhongwei Zhang, Mingyu Shao, Liping Wang, Sujuan Shao + 2 more
'Reinaldo Martinez Palhares'] As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain…
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…
Jose Mejia, Boris Mederos, Nelly Gordillo, Leticia Ortega
The adaptive filtering theory has been extensively developed, and most of the proposed algorithms work under the assumption of Euclidean space. However, in many applications, the data to be processed comes from a non-linear manifold. In this article, we propose an alternative adaptive filter that works on a manifold…
Johannes Schwab, Stephan Antholzer, Markus Haltmeier
Deep learning and (deep) neural networks are emerging tools to address inverse problems and image reconstruction tasks. Despite outstanding performance, the mathematical analysis for solving inverse problems by neural networks is mostly missing. In this paper, we introduce and rigorously analyze families of deep…
Yunfei Huang, Christoph Schell, Tobias B. Huber, Ahmet Nihat Şimşek + 4 more
'Nils Hersch' 'Rudolf Merkel' 'Gerhard Gompper' 'Benedikt Sabass'] Adherent cells exert traction forces on to their environment which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures during developmental morphogenesis. Traction force microscopy (TFM) enables the…
Fereshteh S. Bashiri, Ahmadreza Baghaie, Reihaneh Rostami, Zeyun Yu + 1 more
Multi-modal image registration is the primary step in integrating information stored in two or more images, which are captured using multiple imaging modalities. In addition to intensity variations and structural differences between images, they may have partial or full overlap, which adds an extra hurdle to the…
Attila Gábor, Julio R. Banga
Background Dynamic modelling provides a systematic framework to understand function in biological systems. Parameter estimation in nonlinear dynamic models remains a very challenging inverse problem due to its nonconvexity and ill-conditioning. Associated issues like overfitting and local solutions are usually not…