PPT - æ°æ®å·¥ç¨ä¸ç¥è¯å·¥ç¨æè²é¨éç¹å®éªå®¤

More documents

Recommendations

Info

Group Sparse Topical Coding Group Lasso words codes topics Group Lasso Code to Topics ⎡⎡ ⎢⎢ ⎤⎤ w ⎥⎥ E[ w | w ] || I || I || I K || I nk || I K nk nk nk kn n= 1 n= 1 n= 1 k= 1 n= 1 θ k = E ⎢⎢ | w ⎥⎥= = || I K nk || I || ⎢⎢ ∑∑ I K n= 1 k= 1 ⎥⎥ ⎢⎢∑∑wnk ⎥⎥ ∑wn ∑∑snkβkn n= 1 k= 1 n= 1 n= 1 k= 1 ⎣⎣ ∑ ∑ ∑∑ ∑ ⎦⎦ s β Moran’s Property! Sums of Poisson!
Results • Datasets – 20-newsgroup Time Efficiency • 18,846 documents • 26,214 distinct words • 20 related categories • Baseline methods – Probabilistic model: LDA – Non-Probabilistic model: NMF – Sparse topic model: STC Topic sparsity Classification Accuracy
Page 1 and 2: Big Data: Science or Gimmick! 程
Page 3 and 4: Is Big Data Big Value Predicti
Page 5 and 6: Big Challenges Exist in Big Da
Page 7 and 8: Topics in this talk Data Comple
Page 9 and 10: Topic Modeling meets Big Data
Page 11 and 12: Sparse and Meaningful Topics • L
Page 13: Group Sparse Topical Coding Group
Page 17 and 18: Short texts are prevalent Uncov
Page 19 and 20: Key idea of our approach • T
Page 21 and 22: Comparison between different models
Page 23 and 24: Evaluation on Baidu Zhidao • Dat
Page 25 and 26: Ranking meets Big Data High com
Page 27 and 28: Top-‐k Learning to Rank Ran
Page 29 and 30: Top-‐k Ranking Framework Top
Page 31 and 32: Problems • Predict the percolati
Page 33 and 34: Challenges in the era of Big Data
Page 35 and 36: Can be useful on Social Networ
Page 37 and 38: Case1: Query the Real People o
Page 39 and 40: Summary • Three ScienAfic Chal

PPT - æ°æ®å·¥ç¨ä¸ç¥è¯å·¥ç¨æè²é¨éç¹å®éªå®¤

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?

PPT - æ°æ®å·¥ç¨ä¸ç¥è¯å·¥ç¨æè²é¨éç¹å®éªå®¤