How can I explore high-dimensional data?

How can I explore high-dimensional data?

High-dimensional data hinder sample visualiza-tion and limit exploration of data 1. In these cases, we can make use of multivariate analysis techniques, such as Factor Analysis (FA) and/or Principal Component Analysis (PCA), to reduce a complex data set to one of lower dimensions so as to reveal any hidden features and simplify understanding. In order to render interpretation of FA and PCA easi...

How a Robot Can Explore an Infinite Graph

The Roomba rolls around your house and vacuums the carpets. Early models were known to get stuck in corners or to end up running in circles. If the robot has unlimited memory then you could just give it a map of the room and it would never get trapped or confused. In practice, the robot has limited memory, and for AI programmers, the less used the better. We want to model this problem mathemati...

How many oblivious robots can explore a line

We consider the problem of exploring an anonymous line by a team of k identical, oblivious, asynchronous deterministic mobile robots that can view the environment but cannot communicate. We completely characterize sizes of teams of robots capable of exploring a n-node line. For k < n, exploration by k robots turns out to be possible, if and only if either k = 3, or k ≥ 5, or k = 4 and n is odd....

Methods for regression analysis in high-dimensional data

By evolving science, knowledge and technology, new and precise methods for measuring, collecting and recording information have been innovated, which have resulted in the appearance and development of high-dimensional data. The high-dimensional data set, i.e., a data set in which the number of explanatory variables is much larger than the number of observations, cannot be easily analyzed by ...

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