Dimensionality Reduction Processing of High-dimensional Mobile Medical Data Based on PCA Method
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.
万方数据
维普
