Several techniques are employed for data simplification in nanotechnology:
Dimensionality reduction: Techniques like Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) reduce the number of variables under consideration. Data aggregation: Combining multiple data points to form a summary metric, such as averaging measurements over time or space. Filtering: Removing noise and irrelevant data points to focus on significant information. Feature selection: Identifying and using only the most relevant features of the data for analysis.
