Financial Data Reduction and Information Retention Strategy based on Principal Component Analysis (PCA) Algorithm

As the complexity and dimensionality of financial market data continue to increase, how to reduce the dimensionality while ensuring data validity has become an important challenge in financial data analysis. To this end, this paper introduces a financial data dimensionality reduction and information retention strategy based on the principal component analysis (PCA) algorithm, aiming to improve data processing efficiency and information retention. First, based on the intrinsic characteristics and correlation of financial data, this paper proposes a dimensionality reduction method for dynamically selecting principal components. Secondly, this paper introduces the weighted variance contribution rate standard, taking into account the industry relevance and time series characteristics of financial data, and optimizing the standard for information retention. Finally, in order to further improve the information retention effect, this paper designs a dimensionality reduction algorithm based on minimizing information loss. This algorithm achieves dynamic control of information loss during dimensionality reduction by adaptively adjusting component weights. The experimental results show that after reducing the dimension of financial data, the weighted PCA method can significantly improve the information retention, especially when dealing with data with time series fluctuations and industry differences, compared with traditional PCA, the information retention is improved by about 6%. At the same time, the prediction performance of data based on weighted PCA dimension reduction in multiple machine learning models has also been significantly improved. In the above data conclusions, the dimensionality reduction method proposed in this paper can effectively improve the accuracy and efficiency of financial data analysis, and has high practical application value.