Return-Path: X-Original-To: apmail-spark-issues-archive@minotaur.apache.org Delivered-To: apmail-spark-issues-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id 248D817879 for ; Fri, 17 Jul 2015 08:24:05 +0000 (UTC) Received: (qmail 36324 invoked by uid 500); 17 Jul 2015 08:24:05 -0000 Delivered-To: apmail-spark-issues-archive@spark.apache.org Received: (qmail 36301 invoked by uid 500); 17 Jul 2015 08:24:05 -0000 Mailing-List: contact issues-help@spark.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Delivered-To: mailing list issues@spark.apache.org Received: (qmail 36292 invoked by uid 99); 17 Jul 2015 08:24:05 -0000 Received: from arcas.apache.org (HELO arcas.apache.org) (140.211.11.28) by apache.org (qpsmtpd/0.29) with ESMTP; Fri, 17 Jul 2015 08:24:05 +0000 Date: Fri, 17 Jul 2015 08:24:04 +0000 (UTC) From: "Feynman Liang (JIRA)" To: issues@spark.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Created] (SPARK-9134) LDA Asymmetric topic-word prior MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 Feynman Liang created SPARK-9134: ------------------------------------ Summary: LDA Asymmetric topic-word prior Key: SPARK-9134 URL: https://issues.apache.org/jira/browse/SPARK-9134 Project: Spark Issue Type: Improvement Components: MLlib Reporter: Feynman Liang SPARK-8536 generalizes LDA to asymmetric document-topic priors, which [Wallach et al|http://dirichlet.net/pdf/wallach09rethinking.pdf] proposes offers greater utility in terms of asymmetric priors. However, [Stanford NLP|http://nlp.stanford.edu/software/tmt/tmt-0.2/scaladocs/scaladocs/edu/stanford/nlp/tmt/lda/LDA.html] also permits asymmetric priors on the topic-word prior. We should not support manually specifying the entire matrix (which has numTopics * vocabSize entries); rather we should follow Stanford NLP and take a single vector of length vocabSize for a prior over words and assume that all topics share this prior (e.g. replicate it numTopics times to get the topic-word prior matrix). We are leaving this as todo; any users who have a need for this feature should discuss on this JIRA. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org For additional commands, e-mail: issues-help@spark.apache.org