Data Science and Machine Learning (Theory and Projects) A to Z - Feature Extraction: PCA For Small Sample Size Problems(

The video tutorial explains the concept of Principal Component Analysis (PCA) with a focus on small sample size problems, where the number of dimensions exceeds the number of samples. It discusses the challenges of computing eigenvalues and eigenvectors in such cases and introduces dual PCA as a solution. The tutorial provides a step-by-step procedure for implementing dual PCA, which involves computing eigenvectors of a smaller matrix to indirectly obtain the desired eigenvectors. The video concludes with a brief mention of kernel PCA as a future topic.

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