PPT-Dimensionality Reduction:

SVD amp CUR Mining of Massive Datasets Jure Leskovec Anand Rajaraman Jeff Ullman Stanford University httpwwwmmdsorg 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. 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, 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. 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. Dimensionality Reduction. Author: . Christoph. . Eick. The material is mostly based on the . Shlens. PCA. Tutorial . http://www2.cs.uh.edu/~. ceick/ML/pca.pdf. . and . to a lesser extend based on material . Roselyn. . Sands. Thomas . McCabe. 1. Context van de opdracht. 2. (. Remember. Darwin?). Survival. of the . Fittest. :. How « fit » are . you. ?. Do . you. « . work. smart »:. Right . person. http://www.cs.nyu.edu/yann (November2005.ToappearinCVPR2006)AbstractDimensionalityreductioninvolvesmappingasetofhighdimensionalinputpointsontoalowdimensionalmani-foldsothat Brendan and Yifang . April . 21 . 2015. Pre-knowledge. We define a set A, and we find the element that minimizes the error. We can think of as a sample of . Where is the point in C closest to X. . Principle Component Analysis. Why Dimensionality Reduction?. It becomes more difficult to extract meaningful conclusions from a data set as data dimensionality increases--------D. L. . Donoho. Curse of dimensionality. Presented by: Johnathan Franck. Mentor: . Alex . Cloninger. Outline. Different Representations. 5 Techniques. Principal component . analysis (PCA)/. Multi-dimensional . scaling (MDS). Sammons non-linear mapping. Devansh Arpit. Motivation. Abundance of data. Required storage space explodes!. Images. Documents. Videos. Motivation. Speedup Algorithms. Motivation. Dimensionality reduction for noise filtering. Vector Representation. John A. Lee, Michel Verleysen. 1. Dimensionality Reduction. By: . sadatnejad. دانشگاه صنعتي اميرکبير. (. پلي تکنيک تهران). Dim. Reduction- . Practical Motivations . 2. John A. Lee, Michel Verleysen, . Chapter4 . 1. Distance Preservation. دانشگاه صنعتي اميرکبير. (. پلي تکنيک تهران). 2. The motivation behind distance preservation is that any . Chapter 3. . Data Preprocessing. Jiawei Han, Computer Science, Univ. Illinois at Urbana-Champaign. , 2017. 1. 9/11/17. 2. Chapter 3: Data Preprocessing. Data Preprocessing: An Overview. Data . Cleaning.