基于PCA方法的移动医疗高维数据降维处理

Dimensionality Reduction Processing of High-dimensional Mobile Medical Data Based on PCA Method

移动医疗领域产生大数据集,数据量庞大、数据维度复杂,数据挖掘过程存在诸多冗余数据等问题,本文在此基础上使用主成分分析PCA方法进行降维处理,筛选出有效数据维度,避免数据过度拟合,将高维数据降维成低维数据,对其隐含的结构具有很大帮助,经常用于数据压缩、数据探索以及数据可视化。对移动医疗大数据进行降维处理,不仅能减少数据计算量,也能避免数据过度拟合,有利于医疗数据价值分析,为移动医疗数据集领域的数据挖掘提供新的思路和方法。

In the field of mobile healthcare,there is a large dataset with a large amount of data and complex data dimensions.There are many redundant data issues in the data mining process.Based on this,this article uses principal component analysis(PCA)method for dimensionality reduction to filter out effective data dimensions,avoid overfitting,and reduce high-dimensional data into low-dimensional data,which is very helpful for its implicit structure.It is often used for data compression Data exploration and data visualization.Reducing the dimensionality of mobile medical big data not only reduces the amount of data computation,but also avoids overfitting,which is beneficial for medical data value analysis and provides new ideas and methods for data mining in the field of mobile medical datasets.