A Method of Dimensionality Reduction for Large Scale Data Using PCA
Zhao Gui-r · Computer Knowledge and Technology · 2014
PCA is a general method of linear dimensionality reduction. It is unable to read all the sample data into the memory to do analysis when the data scale becomes large. A method of dimensionality reduction for large scale data using PCA without Hadoop is proposed in this paper. This method solves the problem that it can't do dimensionality reduction directly on large scale data. Practice proves that this method has a good application effect.