From hadoop-commits-return-2343-apmail-lucene-hadoop-commits-archive=lucene.apache.org@lucene.apache.org Tue Aug 28 06:51:13 2007 Return-Path: Delivered-To: apmail-lucene-hadoop-commits-archive@locus.apache.org Received: (qmail 52919 invoked from network); 28 Aug 2007 06:51:13 -0000 Received: from hermes.apache.org (HELO mail.apache.org) (140.211.11.2) by minotaur.apache.org with SMTP; 28 Aug 2007 06:51:13 -0000 Received: (qmail 5901 invoked by uid 500); 28 Aug 2007 06:51:08 -0000 Delivered-To: apmail-lucene-hadoop-commits-archive@lucene.apache.org Received: (qmail 5869 invoked by uid 500); 28 Aug 2007 06:51:08 -0000 Mailing-List: contact hadoop-commits-help@lucene.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: hadoop-dev@lucene.apache.org Delivered-To: mailing list hadoop-commits@lucene.apache.org Received: (qmail 5860 invoked by uid 99); 28 Aug 2007 06:51:08 -0000 Received: from athena.apache.org (HELO athena.apache.org) (140.211.11.136) by apache.org (qpsmtpd/0.29) with ESMTP; Mon, 27 Aug 2007 23:51:08 -0700 X-ASF-Spam-Status: No, hits=-100.0 required=10.0 tests=ALL_TRUSTED X-Spam-Check-By: apache.org Received: from [140.211.11.130] (HELO eos.apache.org) (140.211.11.130) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 28 Aug 2007 06:51:12 +0000 Received: from eos.apache.org (localhost [127.0.0.1]) by eos.apache.org (Postfix) with ESMTP id 2335059A07 for ; Tue, 28 Aug 2007 06:50:52 +0000 (GMT) Content-Type: text/plain; charset="us-ascii" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit From: Apache Wiki To: hadoop-commits@lucene.apache.org Date: Tue, 28 Aug 2007 06:50:52 -0000 Message-ID: <20070828065052.5915.64152@eos.apache.org> Subject: [Lucene-hadoop Wiki] Trivial Update of "Hbase/ShellPlans" by udanax X-Virus-Checked: Checked by ClamAV on apache.org Dear Wiki user, You have subscribed to a wiki page or wiki category on "Lucene-hadoop Wiki" for change notification. The following page has been changed by udanax: http://wiki.apache.org/lucene-hadoop/Hbase/ShellPlans ------------------------------------------------------------------------------ Altools provides automatic parallelization of the most time-consuming relational/matrix/vector operations, and will ensure that the iterative solvers are scalable. + * Survey List - * Parallel Processing of Relational Data + * Parallel Processing of Relational Data - * Parallel Algorithms of Multi-Dimensional Matrix Operations + * Parallel Algorithms of Multi-Dimensional Matrix Operations - * Parallel Gaussian Elimination Algorithm + * Parallel Gaussian Elimination Algorithm ---- @@ -169, +170 @@ }}} = Example Of Altools Use = + == Latent Semantic Analysis by SVD == + Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA was patented in 1988 by Scott Deerwester, Susan Dumais, George Furnas, Richard Harshman, Thomas Landauer, Karen Lochbaum and Lynn Streeter. In the context of its application to information retrieval, it is sometimes called latent semantic indexing (LSI). -- wikipedia - - == LSI by SVD == - Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. - - LSA was patented in 1988 by Scott Deerwester, Susan Dumais, George Furnas, Richard Harshman, Thomas Landauer, Karen Lochbaum and Lynn Streeter. In the context of its application to information retrieval, it is sometimes called latent semantic indexing (LSI). - - This example used SVD decomposition with k=3. + This LSA example used SVD decomposition with k=3. + - [[BR]]''-- The SVD is typically computed using large matrix methods (for example, Lanczos methods) but may also be computed incrementally and with greatly reduced resources via a neural network-like approach which does not require the large, full-rank matrix to be held in memory'' + ''(The SVD is typically computed using large matrix methods (for example, Lanczos methods) but may also be computed incrementally and with greatly reduced resources via a neural network-like approach which does not require the large, full-rank matrix to be held in memory) -- wikipedia'' * ~-'''NOTATION'''-~ * ~-''T,,0,,'' : orthogonal, unit-length columns-~