(I've posted this question on CV, but I feel it would also be great to hear from experts in DS community.)
As a PhD student starting to think about dissertation topics, I am particularly interested in high-dimensional statistical learning. I wish to find some research review/survey/papers (or webpages, blogs, whatever...) about the state-of-the-art research in this research area, but there seems limited resources I can obtain. My first question is then,
- Could you describe some current interesting research topics in high-dimensional statistics? If you can list any relevant resources (papers, webpages, etc.), that would be really helpful.
In addition, I've noticed that high-dimensional statistical learning is closed related with machine learning research. For example, the idea of penalized regularization in high-dimensional statistics was used in machine learning domain, like support vector machine, boosting tree, (sparse) additive models, etc. My question is,
- what are good research papers about the interplay of high-dimensional statistics and machine learning?
Last, since high-dimensional statistics was really motivated by genetic research (like gene-expression analysis, or genome-wide association studies), most of applications in high-dimensional research are devoted in that area.
Are there any successful applications of high-dimensional statistics in areas other than genetics, particularly say, image/text mining, recommendation, etc, areas where machine learning techniques have long been used?
A new question to machine learning researchers/practitioners: I might be wrong, but as I understand, most machine learning algorithms are designed for low-dimensional problems (or at least the number of features is smaller that number of observations). Are there any successful applications of machine learning techniques for modeling high-dimensional data?
Any resources/comments are highly appreciated. Thanks.