Parallelizing LDA Using Hadoop Map-Reduce
PARALLELIZING LDA USING HADOOP MAP-REDUCE (CSCI 596 Project) William Chang Abijit Bej Objective: Parallelize Latent Dirichlet Allocation using Hadoop Map/Reduce framework. Project Abstract: Latent Dirichlet Allocation (LDA) is a generative model for modeling text. The LDA model hypothesizes that documents contain topics with a certain probability, and these topics contain words with a certain pro
PLDA+: Parallel latent dirichlet allocation with data placement and pipeline processing
PLDA+: Parallel Latent Dirichlet Allocation with Data Placement and Pipeline Processing ZHIYUAN LIU, YUZHOU ZHANG, and EDWARD Y. CHANG, Google Inc. MAOSONG SUN, Tsinghua University Previous methods of distributed Gibbs sampling for LDA run into either memory or communication bottlenecks. To improve scalability, we propose four strategies: data placement, pipeline processing, word bundling, and p
Latent Dirichlet Allocations ã® Python 実装 - 木曜ä¸è¶³
LDA ã¨ã¯ "Latent Dirichlet Allocation"。文書ä¸ã®å˜èªžã®ã€Œãƒˆãƒ”ックã€ã‚’確率的ã«æ±‚ã‚る言語モデル。 「潜在的ディリクレé…分法ã€ã¨è¨³ã•ã‚Œã¦ã„ã‚‹ã“ã¨ã‚‚ã‚ã‚‹ãŒã€ãã®åå‰ã ã¨ã€Œãã‚Œã£ã¦ãªã‚“ã ã£ã‘？ã€ã¨ã„ã†äººã®ã»ã†ãŒå¤šãã†ï½—。 å„å˜èªžãŒã€Œéš れトピックã€(話題ã€ã‚«ãƒ†ã‚´ãƒªãƒ¼)ã‹ã‚‰ç”Ÿæˆã•ã‚Œã¦ã„ã‚‹ã€ã¨æƒ³å®šã—ã¦ã€ãã®ãƒˆãƒ”ックを文書集åˆã‹ã‚‰æ•™å¸«ç„¡ã—ã§æŽ¨å®šã™ã‚‹ã“ã¨ãŒã§ãる。特徴ã¯ã€æžœç‰©ã® apple ã¨éŸ³æ¥½ã® apple ã¨ã‚³ãƒ³ãƒ”ュータ関連㮠apple を区別ã™ã‚‹ã“ã¨ãŒå‡ºæ¥ã‚‹(ã“ã¨ãŒæœŸå¾…ã•ã‚Œã‚‹)ã¨ã„ã†ç‚¹ã€‚ãã®ãŸã‚ã«ã€ã©ã®ãƒˆãƒ”ックを生æˆã—ã‚„ã™ã„ã‹ã¨ã„ã†åˆ†å¸ƒã‚’å„æ–‡ç« ã‚‚æŒã¤ã€‚ç´°ã‹ã„話ã¯ç•¥ã€‚ çµæžœã®è¦‹æ–¹ã¨ã—ã¦ã¯ã€å®šé‡çš„ã«ã¯ãƒ‘ープレã‚シティを見るã—(一般ã«å°ã•ã„ã»ã©ã„ã„)ã€å®šæ€§çš„ã«ã¯å„トピックãŒã©ã®ã‚ˆã†ãªå˜èªžã‚’生æˆã™ã‚‹ã‹ã€ãã®ç¢ºçŽ‡ä¸Šä½ã®ã‚‚ã®ã‚’見ã¦ãµã‚€ãµã‚€ã™ã‚‹ã€‚ã“ã®ã€Œå„トピックãŒç”Ÿæˆã™ã‚‹å˜èªžã€
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