强大的凸双集群使用无调方法.
Yifan Chen1, Chunyin Lei2, Chuanquan Li2,3
1Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, USA.
Journal of applied statistics
|February 10, 2025
概括
这项研究引入了一种强大的双聚类算法,有效用于重尾数据,采用一种新的无调节的参数选择方法,提高数据分析的准确性.
科学领域:
- 数据挖掘 数据挖掘
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 双聚类算法对于识别基因表达数据,文本挖掘和推系统中的本地相关性至关重要.
- 传统的双重集群方法通常在重尾数据下失败,限制了它们的应用范围.
- 数据分析的稳定性对于可靠的结果至关重要,特别是在杂或异常倾向的数据集中.
研究的目的:
- 开发一个强大的凸双聚类算法,能够处理大量数据.
- 引入一种高效,无调的方法来选择最佳的强化参数.
- 证明拟议方法在现有双聚类技术上的优越性.
主要方法:
- 使用Huber损失实现了一个强大的凸双聚类算法.
- 开发一种新的无调方法,用于自动选择强化参数.
- 通过模拟研究和现实世界生物医学数据应用的验证.
主要成果:
- 提出的强大的双聚类方法在重型数据上显著优于传统算法.
- 无调节的参数选择方法非常高效和有效.
- 对生物医学数据集的成功应用突出显示了实用的实用性.
结论:
- 强大的凸双聚类算法与休伯损失为重尾数据提供了更好的性能.
- 拟议的无调方法简化了参数选择,提高了可用性.
- 这种方法为各种科学领域的双重集群提供了更可靠的工具.
相关概念视频
Cluster Sampling Method
11.6K
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...
11.6K
Routh-Hurwitz Criterion II
174
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
174
Routh-Hurwitz Criterion I
143
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
143
Quantifying and Rejecting Outliers: The Grubbs Test
1.4K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.4K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
368
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
368
Expected Frequencies in Goodness-of-Fit Tests
2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
2.5K


