What are the main challenges in dimensionality reduction?
An example of an AI chatbot conversation about challenges in dimensionality reduction techniques.
What are the main challenges in dimensionality reduction?
Some key challenges include preserving data variance, avoiding overfitting, and computational efficiency.
Can you explain how overfitting occurs in this context?
Overfitting in dimensionality reduction happens when the model captures noise instead of the underlying data structure.
What techniques can help mitigate these challenges?
Techniques like PCA, t-SNE, and autoencoders can help, but each has its own limitations and trade-offs.
How do I choose the right dimensionality reduction technique for my data?
Consider factors like the size of your dataset, the nature of your data, and the specific goals of your analysis.
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