通过处罚图形对比学习进行图形联合表示集群.
IEEE transactions on neural networks and learning systems
|September 1, 2023
概括
本研究引入了一种使用图形对比学习 (GCL) 的新型图形集群方法,该方法将重建错误最小化,以解决假负样本. 这种方法增强了代表性学习,并改善了集群性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 图形集群对于分析复杂的网络数据至关重要.
- 图形对比学习 (GCL) 是一个占主导地位的范式,但受到假负样本的影响,阻碍了性能.
- 假负样本扭曲了学习的表征,并限制了聚类准确性.
研究的目的:
- 提出一个图形集群方法,以减轻GCL中虚假负样本的影响.
- 通过与输入数据保持相互信息来提高学习表示的质量.
- 为了提高整体图形集群性能.
主要方法:
- 建议在表示和输入之间保持相互信息 (MI),以减少虚假负数导致的语义损失.
- 开发了一种由重建错误处罚的GCL方法,近似MI最大化.
- 设计了一个专门的重建解码器和错误术语来促进聚类.
- 纳入了以伪标签为指导的策略,以进一步完善GCL过程.
主要成果:
- 实验验证证了维持MI的有效性.
- 拟议的GCL方法与重建错误处罚证明了集群性能的改善.
- 这种以伪标签为指导的策略进一步缓解了虚假负样引起的问题.
- 这种新方法的性能超过了最先进的图形集群算法.
结论:
- 拟议的图形集群方法有效地解决了GCL中虚假负样本的挑战.
- 整合重建错误和伪标签指导为高级图形集群提供了一个有希望的方向.
- 该方法显示了需要准确的图形分析的现实应用的巨大潜力.
更多相关视频
12:27Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
7.0K
07:05Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
Published on: October 27, 2016
9.3K
相关概念视频
Vector Algebra: Graphical Method
12.2K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
12.2K
Structural Classification of Joints
3.5K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.5K
Cluster Sampling Method
12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Wilcoxon Signed-Ranks Test for Matched Pairs
160
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
160
Functional Classification of Joints
4.2K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
4.2K
Aggregates Classification
344
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
344
