Landmark techniques

Landmark techniques are methods used in data analysis to transform complex data into simpler, more manageable features. They involve selecting specific, representative data points—called landmarks—that summarize the structure of the entire dataset. By measuring how other data points relate to these landmarks, the techniques create new features that capture important patterns and relationships. This approach helps improve the efficiency and accuracy of machine learning models, especially with large or high-dimensional data, by focusing on these key references rather than the entire dataset.