PDF-Dimensionality and

dimensionalityreduction dimensionality Nuno Vasconcelos ECE De p artment UCSD p Note this course requires a it is responsibility to define it although we can talk If you are too far from this. ustceducn xudongcafangwenjiansun microsoftcom Abstract Making a highdimensional eg 100Kdim feature for face recognition seems not a good idea because it will bring dif64257culties on consequent training computation and stor age This prevents furthe PCA facilitates dimensionality reduction for of64258ine clus tering of users and rapid computation of recommendations For a database of users standard nearestneighbor tech niques require processing time to compute recom mendations whereas Eigentaste Structured Sparsity Models. Volkan Cevher. volkan@rice.edu. Sensors. 160MP. 200,000fps. 192,000Hz. 2009 - Real time. 1977 - 5hours. Digital Data Acquisition. Foundation: . Shannon/Nyquist sampling theorem. Lecture . 8. Data Processing and Representation. Principal Component Analysis (PCA). G53MLE Machine Learning Dr Guoping Qiu. 1. Problems. Object Detection. 2. G53MLE Machine Learning Dr Guoping Qiu. Problems. SVD & CUR. Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. Kenneth D. Harris 24/6/15. Exploratory vs. confirmatory analysis. Exploratory analysis. Helps you formulate a hypothesis. End result is usually a nice-looking picture. Any method is equally valid – because it just helps you think of a hypothesis. Computer Graphics Course. June 2013. What is high dimensional data?. Images. Videos. Documents. Most data, actually!. What is high dimensional data?. Images – dimension 3·X·Y. Videos – dimension of image * number of frames. CunninghamandGhahramanilow-dimensionallinearmappingoftheoriginalhigh-dimensionaldatathatpreservessomefeatureofinterestinthedata.Accordingly,lineardimensionalityreductioncanbeusedforvisualizingorexplor Lydia Song, Lauren Steimle, . Xiaoxiao. . Xu. Outline. Introduction to Project . Pre-processing . Dimensionality Reduction. Brief discussion of different algorithms. K-nearest. D. ecision tree. Logistic regression. And the impossibility of utility maximisation. From indifference…. Concept of subjective utility dates back at least to Aristotle; made central tenet of economics and philosophy by Jeremy Bentham (. Devansh Arpit. Motivation. Abundance of data. Required storage space explodes!. Images. Documents. Videos. Motivation. Speedup Algorithms. Motivation. Dimensionality reduction for noise filtering. Vector Representation. Weiqiang Dong. 1. Function Estimate . Input: . O. utput: . where . (“target function”) is a single valued deterministic function of . and . is a random variable,. The goal is to obtain an . estimate. Lioma. Lecture . 18: Latent Semantic Indexing. 1. Overview. Latent semantic indexing . Dimensionality reduction. LSI in information retrieval. 2. Outline. Latent semantic indexing . Dimensionality reduction. Clustering, Dimensionality Reduction and Instance Based Learning Geoff Hulten Supervised vs Unsupervised Supervised Training samples contain labels Goal: learn All algorithms we’ve explored: Logistic regression