What Techniques are Used to Analyze High Dimensional Data?
Several techniques are employed to analyze high dimensional data in catalysis: - Principal Component Analysis (PCA): PCA reduces the dimensionality of data while retaining most of the variance, making it easier to visualize and analyze. - Machine Learning: Techniques like neural networks and support vector machines can model complex relationships within the data. - Cluster Analysis: This technique helps in identifying groups or clusters of similar data points, which can reveal patterns and relationships in the data. - Regression Analysis: Methods like partial least squares regression (PLSR) are used to understand the relationship between input variables and catalytic performance.
