Sparse Principal Component Analysis (SPCA) is a method used to improve the accuracy of traditional Principal Component Analysis (PCA) when dealing with large amounts of data. SPCA helps to find the right solutions in high-dimensional data analysis by reducing complexity and extracting important features. This technique has been shown to be effective in scientific studies, offering a more accurate approach to data processing and feature extraction compared to regular PCA.