Hello, I am working on a large regression problem that has between 100 and 200 samples, 1000 dimensions, and so on. I am aware that this is a unique issue for which there are no effective solutions.
I’m looking for things that I can use as a foundation, like book chapters, articles, or models or strategies you’ve already used.
Hey pals,
In data science and machine learning, problems with high dimensionality—a large number of features—and a small sample size present unique challenges. The performance and dependability of the model may be greatly impacted by these problems.
“Pattern Recognition and Machine Learning” by Christopher Bishop provides a robust foundation in probabilistic graphical models and kernel methods, which are particularly useful for managing high dimensional data. The book delves into these advanced techniques, offering insights and methodologies that can help address the complexities associated with high dimensional regression problems.