Results 81 to 90 of about 5,853,511 (292)

Structure propagation for zero-shot learning

, 2017
The key of zero-shot learning (ZSL) is how to find the information transfer model for bridging the gap between images and semantic information (texts or attributes).
CH Lampert, E Zhang, G Lin, G Lin, G Lin, G Patterson, GJ Qi, Y Fu, Y Zhang, Z Akata   +9 more
core   +1 more source

Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task

Italian National Conference on Sensors, 2020
A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array.
Jersson X. Leon-Medina, Maribel Anaya, F. Pozo, D. Tibaduiza   +3 more
semanticscholar   +1 more source

Spatio-Temporal Manifold Learning for Human Motions via Long-Horizon Modeling [PDF]

IEEE Transactions on Visualization and Computer Graphics, 2019
Data-driven modeling of human motions is ubiquitous in computer graphics and computer vision applications, such as synthesizing realistic motions or recognizing actions. Recent research has shown that such problems can be approached by learning a natural
He Wang, Edmond S. L. Ho, Hubert P. H. Shum, Zhanxing Zhu   +3 more
semanticscholar   +1 more source

Overview of molecular signatures of senescence and associated resources: pros and cons

FEBS Open Bio, EarlyView.
Cells can enter a stress response state termed cellular senescence that is involved in various diseases and aging. Detecting these cells is challenging due to the lack of universal biomarkers. This review presents the current state of senescence identification, from biomarkers to molecular signatures, compares tools and approaches, and highlights ...
Orestis A. Ntintas, Sylvia Vagena, Pavlos Pantelis, Giorgos Theocharous, Russel Petty, Konstantinos Evangelou, Vassilis G. Gorgoulis   +6 more
wiley   +1 more source

Recovering manifold representations via unsupervised meta-learning

Frontiers in Computer Science
Manifold representation learning holds great promise for theoretical understanding and characterization of deep neural networks' behaviors through the lens of geometries.
Yunye Gong, Jiachen Yao, Ruyi Lian, Xiao Lin, Chao Chen, Ajay Divakaran, Yi Yao   +6 more
doaj   +1 more source

Diversity Multi-View Clustering With Subspace and NMF-Based Manifold Learning

IEEE Access, 2023
Since the complementarity information among multiple views has been exploited to improve the clustering effect significantly, multi-view clustering has become a hot topic, and many multi-view clustering methods have emerged.
Jiaman Ding, Xiaojiang Fang, Lianyin Jia, Ying Jiang, Runxin Li   +4 more
doaj   +1 more source

Plasma Proteomic Signatures for Alzheimer's Disease: Comparable Accuracy to ATN Biomarkers and Cross‐Platform Validation

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Background There is growing recognition of the potential of plasma proteomics for Alzheimer's Disease (AD) risk assessment and disease characterization. However, differences between proteomics platforms introduce uncertainties regarding cross‐platform applicability.
Manyue Hu, Oliver Robinson, Christina M. Lill, Anna Matton, Raquel Puerta, Pilar Sanz, Merce Boada, Agustín Ruiz, Lefkos Middleton, for the Alzheimer's Disease Neuroimaging Initiative   +9 more
wiley   +1 more source

Probability Distribution-Based Dimensionality Reduction on Riemannian Manifold of SPD Matrices

IEEE Access, 2020
Representing images and videos with Symmetric Positive Definite (SPD) matrices and utilizing the intrinsic Riemannian geometry of the resulting manifold has proved successful in many computer vision tasks.
Jieyi Ren, Xiao-Jun Wu
doaj   +1 more source

Latent Semantic Learning with Structured Sparse Representation for Human Action Recognition

, 2011
This paper proposes a novel latent semantic learning method for extracting high-level features (i.e. latent semantics) from a large vocabulary of abundant mid-level features (i.e.
Balasubramanian, Belkin, Blei, Cheng, Donoho, Hofmann, Jenatton, Lafon, Liu, Lu, Niebles, Olshausen, Parameswaran, Tibshirani, Turaga, Wang, Wright, Yan, Yuxin Peng, Zhiwu Lu   +19 more
core   +1 more source

Multilabel Distribution Learning Based on Multioutput Regression and Manifold Learning

IEEE Transactions on Cybernetics, 2020
Real-world multilabel data are high dimensional, and directly using them for label distribution learning (LDL) will incur extensive computational costs.
Chao Tan, Sheng Chen, G. Ji, Xin Geng
semanticscholar   +1 more source