通过共享潜伏子标签结构和同时正交基础集群的多标签特征选择
IEEE transactions on neural networks and learning systems
|April 24, 2024
概括
这项研究引入了SLOFS,一种新的多标签特征选择方法. SLOFS有效地减少了隐藏标签空间中的冗余信息,提高了对高维数据的特征选择的准确性.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 高维多标签数据给特征选择带来了挑战,因为标签噪音大和不完整.
- 现有的方法将标签空间映射到低维的潜在空间,但通常保留冗余信息.
- 隐藏标签空间中的这种冗余性可能会对标签相关性的准确捕获产生负面影响.
研究的目的:
- 提出一种新的方法,SLOFS,用于多标签特征选择,该方法解决了隐藏标签空间中冗余信息的问题.
- 开发一种技术,通过消除冗余信息,有效提取潜在标签相关性.
- 为了提高高维多标签数据集中特征选择的性能.
主要方法:
- 引入了一个隐藏直角基础结构共享 (LOBSS) 术语,以创建一个无冗余的隐藏子标签空间.
- 利用分离的潜伏集群中心来指导LOBSS术语,保留子标签信息和集群结构.
- 采用图形规范化来实现数据和潜在子标签之间的结构一致性,以及动态子标签图形来实现高质量的子标签空间构建.
主要成果:
- 拟议的SLOFS方法有效地消除了隐藏标签空间中的冗余信息.
- LOBSS术语成功指导了非冗余隐藏子标签空间的构建.
- 18个数据集的实验结果表明,SLOFS始终优于现有的特征选择方法.
结论:
- 通过有效管理冗余信息,SLOFS在多标签功能选择方面取得了重大进展.
- 该方法增强了隐藏标签相关性的提取,从而导致更准确的特征选择.
- 与不同数据集的先前方法相比,SLOFS表现出优越且一致的性能.
更多相关视频
相关概念视频
Law of Independent Assortment
55.6K
While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
55.6K
Cluster Sampling Method
11.9K
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...
11.9K
Classification of Systems-II
141
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
141
Structural Classification of Joints
3.4K
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...
3.4K
Classification of Signals
453
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
453
Classification of Systems-I
183
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
183


