Due to various curses of dimensionality, the accuracy and speed of many of the common predictive techniques degrade on high dimensional data. What are some of the most useful techniques/tricks/heuristics that help deal with high-dimensional data effectively? For example,Do certain statistical/modeling methods perform well on high-dimensional datasets?Can we improve the performance of our predictive models on high-dimensional data by using certain (that define alternative notions of distance) or kernels (that define alternative notions of dot product)?What are the most useful techniques of dimensionality reduction for high-dimensional data?
High-dimensional data: What are useful techniques to know?
machine learning;statistics;dimensionality reduction