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 of Computer Science UIUC dengcai2csuiucedu Xiaofei He Yahoo hexyahooinccom Jiawei Han Dept of Computer Science UIUC hanjcsuiucedu Abstract Linear Discriminant Analysis LDA has been a popular method for extracting features which preserve class separa torontoedu Abstract This is a note to explain Fisher linear discriminant analysis 1 Fisher LDA The most famous example of dimensionality reduction is principal components analysis This technique searches for directions in the data that have largest v Fisher Linear Discriminant 2 Multiple Discriminant Analysis brPage 2br CSE 555 Srihari 1 Motivation Projection that best separates the data in a least squares sense CA finds components that are useful for representing data owever no reason to assum ECE, UA. Content. Introduction. Support Vector Machines. Active Learning Methods. Experiments & Results. Conclusion. Introduction. ECG signals represent a useful information source about the rhythm and functioning of the heart.. Given . a quadratic equation use the . discriminant. to determine the nature . of the roots.. What is the discriminant?. The discriminant is the expression b. 2. – 4ac.. The value of the discriminant can be used. 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. 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 . . Support Vector Machine. Courtesy of . Jinwei. . Gu. Today: Support Vector Machine (SVM). A classifier derived from statistical learning theory by . Vapnik. , et al. in 1992. SVM became famous when, using images as input, it gave accuracy comparable to neural-network with . Vocabulary. Discriminant - tells you how many solutions and what type you will have.. If the . discriminant is. . positive . – 2 real solutions. . negative . – 2 imaginary solutions. . zero . – 1 real solution. ECE, UA. ECG signal processing - Case [1]. Diagnosis of Cardiovascular Abnormalities From Compressed ECG: A Data Mining-Based Approach[1]. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 15, NO. 1, JANUARY 2011. . for the given values:.  . 3. Sketch . the graph for each quadratic. No solutions One solution Two solutions. Using the Discriminant. I can . use the discriminant to determine how many solutions a quadratic equation will have.. CS 560 Artificial Intelligence. Many slides throughout the course adapted from Svetlana . Lazebnik. , Dan Klein, Stuart Russell, Andrew Moore, Percy Liang, Luke . Zettlemoyer. , Rob . Pless. , Killian Weinberger, Deva . 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).