4 ms·
The real issue is that inversion / solving is O(n^3). This really hammers you for high dimensional settings (e.g. brain imaging n>100k). Solutions: * Pick a
by conjectures 7y ago
The real issue is that inversion / solving is O(n^3).
This really hammers you for high dimensional settings (e.g. brain imaging n>100k).
Solutions:
* Pick a very special kernel with an known / approximable solution.
* Reduce the dimension of the problem (sparse gps).
- bhl 7y agoThe n parameter there was the number of datapoints, not the size of the inputs (that would've been way worse). For brain imaging, is the 100k parameter also referencing datapoints, or dimensionality of the input? Chapter 8 of the Rasmussen reference does talk about approximation methods for large datasets, but I couldn't find any reference implementations to go off of.
- conjectures 7y ago"number of datapoints, not the size of the inputs" For brain imaging, 100k was 'the dimensionality of the input' (but also the number of datapoints). This could easily be O(10^6) though with higher res imaging. I'd check out, for discussion: https://www.prowler.io/blog/sparse-gps-approximate-the-posterior-not-the-model https://www.prowler.io/blog/sparse-gps-approximate-the-poste... For code: https://github.com/GPflow/GPflow https://github.com/GPflow/GPflow Based on the people involved knowing what they are talking about, rather than on experience using that particular work.