PPT-On-line LDA

Pilfered from NIPS 2010 Online Learning for LDA Hoffman Bach amp Blei. PCA Limitations of LDA Variants of LDA Other dimensionality reduction methods brPage 2br CSCE 666 Pattern Analysis Ricardo Gutierrez Osuna CSETAMU Linear discriminant analysis two classes Objective LDA seeks to reduce dimensionality while preserv Linear . Discriminant. Analysis. Chaur. -Chin Chen. Institute of Information Systems and Applications. National . Tsing. . Hua. University. Hsinchu. . 30013, Taiwan. E-mail: cchen@cs.nthu.edu.tw. Decorelation. for clustering and classification. . ECCV 12. Bharath. . Hariharan. , . Jitandra. Malik, and Deva . Ramanan. Motivation. State-of-the-art Object Detection . HOG. Linear SVM. Model Precision 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 50 topics CTM LDA pLSI 100 topics CTM LDA pLSI 150 topics CTM LDA pLSI New York Times Wikipedia Topic Log Odds -5 -4 -3 -2 -1 0 -7 -6 -5 Dirichlet. Allocation. 1. Directed Graphical Models. William W. Cohen. Machine Learning 10-601. 2. DGMs: . The . “. Burglar Alarm. ”. example. Your house has a twitchy burglar alarm that is also sometimes triggered by earthquakes.. Linear Discriminant Analysis. Objective. -Project a . feature space (a dataset n-dimensional samples) onto a smaller . -Maintain . the . class separation. Reason. -Reduce computational costs. -Minimize . Coast Guard . District. ,. . . . Guide:. P. Durga Prasad. Presented By:. M. . . Prabhakar. (13FF1A0501. ). S. . Pravallika. (13FF1A0504. ). S. . Vijaya. Nirmala (13FF1A0506. ). CONTENTS. ABSTRACT . INTRODUCTION. EXISTING SYSTEM. PROPOSED SYSTEM. in LDA and CTM. William M. Pottenger, Ph.D., Paul B. Kantor, Ph. D.. Kashyap. . Kolipaka. , . Shenzhi. Li and . Nir. . Grinberg. Rutgers University. 10/16/2010. 1. What is LDA all about?. 10/16/2010. ab. -initio theory of correlated materials . The challenge of the iron . pnictides. .. Gabriel . Kotliar. Physics Department and Center for Materials Theory Rutgers University. Work with K. Haule Z. Ying A. . Action:. . Manage a Limited Depositary Account. Conditions: . . In a classroom environment given a two hour time frame; students will work as a member of a small group/ individual; using DoD FMR 7000.14-R, Volume 5 Chapters 1-3 & 14, FM 1-06, the slide presentation for immediate referencing, and students are also required to participate in small group . Analysis. ). ShaLi. . Limitation of PCA. The direction of maximum variance is not always good for classification. Limitation of PCA. The direction of maximum variance is not always good for classification. Dongyeop. Kang. 1. , Youngja Park. 2. , Suresh . Chari. 2. . 1. . . IT Convergence Laboratory, KAIST . Institute,Korea. 2. . IBM T.J. Watson Research . Center, NY, USA. kindly visit us at www.nexancourse.com. Prepare your certification exams with real time Certification Questions & Answers verified by experienced professionals! We make your certification journey easier as we provide you learning materials to help you to pass your exams from the first try.