Discovering dimensionality reduction techniques
Dimensionality reduction is a technique used in machine learning and data analysis to reduce the number of variables or features in a dataset. The goal of dimensionality reduction is to simplify the data while retaining important information, thereby improving the efficiency and effectiveness of subsequent analysis tasks.
High-dimensional datasets can be challenging to work with due to several reasons:
- Curse of dimensionality: As the number of features increases, the data becomes more sparse, making it difficult to find meaningful patterns or relationships
- Computational complexity: Many algorithms and models become computationally expensive as the dimensionality of the data increases, requiring more time and resources for analysis
- Overfitting: High-dimensional data is more susceptible to overfitting, where a model becomes too specialized to the training data and fails to generalize well to new data
Dimensionality...
