Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
Associative Learning01:27

Associative Learning

317
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
317
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

105
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
105
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

1.0K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.0K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

88
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
88
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.0K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A Randomized Controlled Trial of Yizhi Kaiqiao Formula Combined With Repetitive Transcranial Magnetic Stimulation on Neurocognitive and Social Outcomes in Preschool Children With Autism Spectrum Disorder.

Developmental neurobiology·2026
Same author

Advancing high-altitude medicine: a model for the future.

Signal transduction and targeted therapy·2026
Same author

<sup>68</sup>Ga-Labeled LLP2A for PET Imaging of Very Late Antigen-4 in Acute Cardiac Rejection.

Molecular pharmaceutics·2026
Same author

Deciphering Object Concepts: Hierarchical Cross-Modal Relational Reasoning for Mining Object-Attribute-Affordance Associations.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Validating a gamified size perception task for identifying cognitive profiles in children: a latent profile analysis of executive function and sensory measures.

Frontiers in psychology·2026
Same author

DARS2 serves as an independent prognostic factor and participates in multiple biological processes in bladder urothelial carcinoma.

Translational andrology and urology·2026
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same journal

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

IEEE transactions on neural networks and learning systems·2026
Same journal

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: Jun 14, 2025

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging
11:24

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

Published on: December 12, 2012

13.6K

基于直角NMF的压缩软标签学习

Fangfang Li, Quanxue Gao, Qianqian Wang

    IEEE transactions on neural networks and learning systems
    |September 2, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了一种使用非负矩阵因子化 (NMF) 的新型多视图集群方法,该方法结合了空间结构和适应权重. 这种方法直接产生了集群标签,提高了高维数据分析的稳定性和可解释性.

    更多相关视频

    Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    989
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    4.7K

    相关实验视频

    Last Updated: Jun 14, 2025

    Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging
    11:24

    Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

    Published on: December 12, 2012

    13.6K
    Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    989
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    4.7K

    科学领域:

    • 数据科学数据科学数据科学
    • 机器学习 机器学习
    • 计算机视觉 计算机视觉

    背景情况:

    • 多视图高维数据分析在各个领域都至关重要.
    • 非负矩阵因子化 (NMF) 是一种用于集群此类数据的常见技术.
    • 现有的基于NMF的方法经常忽视空间结构,需要后处理,限制稳定性.

    研究的目的:

    • 开发一个更稳定和可解释的多视图集群算法.
    • 通过结合空间信息来解决现有的NMF方法的局限性.
    • 为了使集群标签在没有后处理的情况下直接提取.

    主要方法:

    • 提出了一种新的方法,将来自不同观点的系数矩阵组成的张量最小化沙顿p-规范.
    • 在集群索引矩阵上嵌入直角约束,用于稀疏性和可解释性.
    • 使用适应性权重来区分不同观点的重要性.
    • 开发了一个用于模型解决和分析的无监督优化方案.

    主要成果:

    • 拟议的方法通过考虑系数矩阵中的空间结构,有效地捕获互补信息.
    • 直角约束导致了稀疏的集群索引矩阵,允许直接获取标签.
    • 适应性权重改善了对不同视图的重要性的处理.
    • 对六个基准数据集的实证评估表明,与现有算法相比,它们的性能优越.

    结论:

    • 新的多视图集群方法提供了更好的稳定性和可解释性.
    • 考虑空间结构和采用适应权重是增强基于NMF的聚类的关键.
    • 该方法提供了一种直接而强大的方法,可以从高维多视图数据中获得集群标签.