PPT-LDA ( Linear Discriminant

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. 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 N is the process noise or disturbance at time are IID with 0 is independent of with 0 Linear Quadratic Stochastic Control 52 brPage 3br Control policies statefeedback control 0 N called the control policy at time roughly speaking we choo Matthew Brown. University of British Columbia. (prev.) Microsoft Research. [ Collaborators: . †. Simon Winder, *Gang . Hua. , . †. Rick . Szeliski. . †. =MS Research, *=MS Live Labs]. Applications @MSFT. I. Standard Form of a quadratic. In form of . Lead coefficient (a) is positive..  .  .  . Examples.  . II. Discriminant. Tells us about nature . of. roots of a quadratic. 4 cases: 1. If D>0, then 2 real roots.. Why do we use the discriminant?. The discriminant tells us one of two things:. How many roots/x-intercepts/zeros does a quadratic function have?. How many solutions does a quadratic equation have?. Example. Decorelation. for clustering and classification. . ECCV 12. Bharath. . Hariharan. , . Jitandra. Malik, and Deva . Ramanan. Motivation. State-of-the-art Object Detection . HOG. Linear SVM. Source: “Topic models”, David . Blei. , MLSS ‘09. Topic modeling - Motivation. Discover topics from a corpus . Model connections between topics . Model the evolution of topics over time . Image annotation. 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 Alexander Kotov. 1. , . Mehedi. Hasan. 1. , . April . Carcone. 1. , Ming Dong. 1. , Sylvie Naar-King. 1. , Kathryn Brogan Hartlieb. 2. . 1 . Wayne State University. 2 . Florida International University. Recall, we have used the quadratic formula previously. Gives the location of the roots (x-intercepts) of the graph of a parabola. Function must be in standard form; f(x) = ax. 2. + . bx. + c. Example. Find the roots for the function f(x) = 2x. . (. not. including the radical sign) in the quadratic formula is called the . . , . D. , of the corresponding quadratic equation, . ..  . The discriminant allows you to determine the nature of the roots of the equation because. Mohammad Ali . Keyvanrad. Machine Learning. In the Name of God. Thanks to: . M. . . Soleymani. (Sharif University of Technology. ). R. . Zemel. (University of Toronto. ). p. . Smyth . (University of California, Irvine). Data Mining for Business Analytics. Shmueli. , Patel & Bruce. Discriminant Analysis: Background. A classical statistical technique. Used for classification long before data mining. Classifying organisms into species. ISC2017. 20. th. Information Security Conference. 22-24. th. November, 2017. Ho Chi Minh, Viet Nam. Lam Tran. 1,3. , . Thang. Hoang. 2. , . Thuc. Nguyen. 3. , . Deokjai. Choi. 1. 1. ECE, . Chonnam.