半监督时间序列分类的双向一致性与时间意识
Han Liu1, Fengbin Zhang1, Xunhua Huang1
1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.
概括
本研究引入了一种新的半监督学习框架,用于时间序列分类. 它通过学习时间表示来改善特征分离,从而导致更具歧视性的类界限.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 半监督学习 (SSL) 有效地减少了对标记数据的依赖.
- 现有的SSL方法经常忽视时间动态,阻碍时间序列的特征空间分离.
- 伪标记是利用未标记的时间序列数据的一个常见技术.
研究的目的:
- 提出一个新的半监督时间序列分类框架,TS-BCT.
- 通过整合时间表示来增强特征空间的分离性.
- 在时间序列分类任务中提高类界限的区分能力.
主要方法:
- 通过双向一致性与时间意识 (TS-BCT) 开发了一种半监督的时间序列分类框架.
- 采用特定时间增强来创建原始时间序列数据的两个不同的视图.
- 利用伪标签指导的对比学习与时间意识模块来规范特征分布.
- 实施了一种双向一致性战略,从多个角度整合伪标签.
主要成果:
- 通过学习时间表示,TS-BCT有效地规范了特征空间分布.
- 该框架产生了歧视性的时间不变表示.
- 双向的一致性策略导致了分隔良好的特征空间和更具歧视性的类界限.
- 在现实数据集上的实验结果表明,TS-BCT的表现优于现有方法.
结论:
- TS-BCT为半监督时间序列分类提供了一种有效的方法.
- 学习时间表示对于改善时间序列在SSL中的特征分离至关重要.
- 与基线方法相比,拟议的框架显示出更高的性能.
相关概念视频
01:31Classification of Systems-II
137
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,
137
00:54Time-Series Graph
4.3K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.3K
01:03Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K
01:29Aggregates Classification
306
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...
306
01:30Classification of Signals
420
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...
420
01:26Classification of Systems-I
177
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:
177