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I'm surprised that there aren't any PCA/Feature-Extraction/Dimension-Reduce algos on this list. And there's also a really strong emphasis on clustering and clas
by tdaltonc 13y ago
I'm surprised that there aren't any PCA/Feature-Extraction/Dimension-Reduce algos on this list. And there's also a really strong emphasis on clustering and classification.
Is this an artifact of how the list was put together or is clustering really that much more common than feature extraction? Is PCA thought to be too trivial to qualify?
- micro_cam 13y agoPageRank is a similar eigenvalue based algorithm and I think PCA can be seen as a relaxation of k-means so they sort of did. But yes, if i wrote the list it eigenvector based methods would be more prominent. Also If I wrote it the tree based methods would have been replaced with random forests which are similar to CART but use the crucial ideas of sample bagging and random feature subsets which have proven themselves over and over. I think it may just be out of date.
- jessaustin 13y agoAny suggestions for something more recent?
- micro_cam 13y agoThe machine learning reddit recently started a faq: http://www.reddit.com/r/MachineLearning/comments/20i0vi/meta_collection_of_links_for_beginners_faq/ http://www.reddit.com/r/MachineLearning/comments/20i0vi/meta... They also have a good selection of articles though they tend to be somewhat deep learning/neural network focused. These algorithms have seen a big surge in interest and success of late but may not be as applicable to "data mining" (as opposed to say image or speech recognition).
- aet 13y agoI think PCA is better understood in theorem-proof style of mathematics rather than algorithmically. For example, PCA can be understood as an optimization problem. Whereas CART and k-NN and the likes are best understood algorithmically. I know k-NN was introduced as an algorithm and only later figured out in terms of probability, etc..