Hi all, I've created a new JIRA.
https://issues.apache.org/jira/browse/SPARK-23437 All concerned are welcome to discuss. Best, Valeriy. On Sat, Feb 3, 2018 at 9:24 PM, Valeriy Avanesov <acop...@gmail.com> wrote: > Hi, > > no, I don't thing we should actually compute the n \times n matrix. Leave > alone inverting it. However, variational inference is only one of the many > sparse GP approaches. Another option could be Bayesian Committee. > > Best, > > Valeriy. > > > > On 02/02/2018 09:43 PM, Simon Dirmeier wrote: > >> Hey, >> >> I wanted to see that for a long time, too. :) If you'd plan on >> implementing this, I could contribute. >> However, I am not too familiar with variational inference for the GPs >> which is what you would need I guess. >> Or do you think it is feasible to compute the full kernel for the GP? >> >> Cheers, >> S >> >> >> >> Am 01.02.18 um 20:01 schrieb Valeriy Avanesov: >> >>> Hi all, >>> >>> it came to my surprise that there is no implementation of Gaussian >>> Process in Spark MLlib. The approach is widely known, employed and scalable >>> (its sparse versions). Is there a good reason for that? Has it been >>> discussed before? >>> >>> If there is a need in this approach being a part of MLlib I am eager to >>> contribute. >>> >>> Best, >>> >>> Valeriy. >>> >>> >>> --------------------------------------------------------------------- >>> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org >>> >>> >> >
