PPT-Dimensionality Reduction

UV Decomposition SingularValue Decomposition CUR Decomposition Jeffrey D Ullman Stanford University Reducing Matrix Dimension Often our data can be represented by an mbyn matrix. 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 Saul Kilian Q Weinberger Fei Sha Jihun Ham Daniel D Lee How can we search for low dimensional structure in high dimensional data If the data is mainly con64257ned to a low dimensional subspace then simple linear methods can be used to discover the s 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. 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. Presented by: Johnathan Franck. Mentor: . Alex . Cloninger. Outline. Different Representations. 5 Techniques. Principal component . analysis (PCA)/. Multi-dimensional . scaling (MDS). Sammons non-linear mapping. 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 . 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. 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. . CISC 5800. Professor Daniel Leeds. The benefits of extra dimensions. Finds existing complex separations between classes. 2. The risks of too-many dimensions. 3. High dimensions with kernels over-fit the outlier data. Presented by: Johnathan Franck. Mentor: . Alex . Cloninger. Outline. Different Representations. 5 Techniques. Principal component . analysis (PCA)/. Multi-dimensional . scaling (MDS). Sammons non-linear mapping. is an important tool in machine learning/data mining, we must always be aware that it can distort the data in misleading ways.. Above is a two dimensional projection of an intrinsically three dimensional world….. Devansh Arpit. Motivation. Abundance of data. Required storage space explodes!. Images. Documents. Videos. Motivation. Speedup Algorithms. Motivation. Dimensionality reduction for noise filtering. Vector Representation. 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