持久的拉普拉西安增强算法用于几乎没有标记的数据分类
Gokul Bhusal1, Ekaterina Merkurjev1,2, Guo-Wei Wei1,3,4
1Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA.
概括
本研究介绍了一种使用代数拓和图形理论的新型半监督学习 (SSL) 方法. 持续的拉普拉西亚增强图形MBO显著减少了机器学习任务中标记数据的需求.
科学领域:
- 机器学习 机器学习
- 计算拓学的计算拓学
- 数据科学数据科学数据科学
背景情况:
- 监督机器学习 (ML) 需要大量的标记数据,这往往是昂贵的和耗时的获取.
- 半监督学习 (SSL) 利用标记和未标记的数据来缓解数据采集挑战.
- 基于图形的SSL方法是有效的,但可能是计算密集的.
研究的目的:
- 开发一种高效的半监督学习方法,尽量减少对标记数据的要求.
- 将代数拓与基于图的技术集成在一起,以提高ML性能.
- 解决医疗分析和自然语言处理等领域的数据稀缺问题.
主要方法:
- 提出了一种新的方法:持续的拉普拉西亚增强图形MBO.
- 集成的持久光谱图理论与梅里曼-本斯-奥舍尔 (MBO) 方案.
- 利用过来产生链复合体和简化复合体,构建持久的拉普拉西安.
主要成果:
- 拟议的方法表现出高效率,与传统的ML技术相比,需要标记的数据要少得多.
- 该方法适用于小型和大型数据集.
- 在分类任务上进行评估时,持久的拉普拉斯增强图形MBO超过了现有的半监督算法.
结论:
- 持续的拉普拉斯增强图形MBO为半监督学习提供了一种强大且数据效率高的方法.
- 这种方法为具有有限标记数据的应用提供了有价值的替代方案.
- 代数拓学的集成增强了基于图的SSL的功能.
相关概念视频
Classification of Systems-II
458
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,
458
Classification of Systems-I
552
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:
552
Residuals and Least-Squares Property
9.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.1K
Classification of Signals
1.3K
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...
1.3K
Aggregates Classification
970
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...
970
Survival Tree
388
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
388
