'Principle components analysis' presentation slideshows

March 2008

Gauteng Wealth Survey. March 2008.

1.46k views • 122 slides

Clustering Tutorial

927 views • 60 slides

Classification ( SVMs / Kernel method)

Classification ( SVMs / Kernel method). LP versus Quadratic programming. LP: linear constraints, linear objective function LP can be solved in polynomial time. In QP, the objective function contains a quadratic form. For +ve semindefinite Q, the QP can be solved in polynomial time.

728 views • 56 slides

642 views • 46 slides

Lecture 7: Image Processing and Interpretation

810 views • 46 slides

Computer Vision

540 views • 38 slides

Syllabus

Syllabus. Reference Book. We covered Regression in Applied Stats. We will review Regression and cover Time Series and Principle Components Analysis. . Reference Book. Probabilities. Probability Distribution. Conditional Probability & Bayesian Networks. Linear Regression. More Regression.

454 views • 26 slides

Face Recognition

562 views • 22 slides

Morphometric Variation in Ancient and Modern Sus scrofa

371 views • 20 slides

Factor Analysis I

348 views • 13 slides

Learning the parts of objects by nonnegative matrix factorization

Learning the parts of objects by nonnegative matrix factorization. D.D. Lee from Bell Lab H.S. Seung from MIT Presenter: Zhipeng Zhao. Introduction. NMF (Nonnegative Matrix Factorization): Theory: Perception of the whole is based on perception of its parts.

311 views • 10 slides

Principle Components Analysis

Principle Components Analysis. A method for data reduction. Factor Analytic Techniques. Reduce the number of variables Detect structure in the relationships among variables. Principal Factor Analysis (Common Factor Analysis). A method for detecting structure Y = XB + E

203 views • 8 slides