Dimensionality Reduction
Dimensionality reduction is a technique used in machine learning and data analysis to reduce the number of input variables or features in a dataset while preserving as much relevant information as possible. It simplifies complex data, reduces computation time, and helps avoid problems like overfitting and the curse of dimensionality.
In many real-world applications, datasets have a large number of features, some of which may be irrelevant or redundant. Dimensionality reduction helps in identifying and removing these, making the data easier to visualize and analyze.
There are two main approaches:
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Feature Selection – Selecting a subset of the most important features.
Example: Removing less informative questions from a survey dataset. -
Feature Extraction – Transforming data into a lower-dimensional space.
Example: -
PCA (Principal Component Analysis): Reduces dimensions by projecting data onto a new set of orthogonal axes.
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t-SNE: Used for visualizing high-dimensional data in 2D or 3D.
Examples:
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In face recognition, reducing image features before classification.
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In text processing, reducing thousands of word features using techniques like LSA (Latent Semantic Analysis).
Conclusion:
Dimensionality reduction helps improve model performance and interpretability by simplifying the data without losing important information.
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