PPT-Dimensionality reduction

k Ramachandra murthy Why Dimensionality Reduction It is so easy and convenient to collect data Data is not collected only for data mining Data accumulates in an unprecedented speed Data preprocessing . 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 We extend this concept to robotic hands and show how a similar dimensionality reduction can be de64257ned for a number of different hand models This framework can be used to derive planning algorithms that produce stable grasps even for highly compl JP van der Maaten EO Postma HJ van den Herik MICC Maastricht University PO Box 616 6200 MD Maastricht The Netherlands Abstract In recent years a variety of nonlinear dimensionality reduction techniques have been proposed many of which rely on the 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. . local. image . descriptors. . into. . compact. . codes. Authors. :. Hervé. . Jegou. Florent. . Perroonnin. Matthijs. . Douze. Jorge. . Sánchez. Patrick . Pérez. Cordelia. Schmidt. Presented. 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. 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. . Kenneth D. Harris. April 29, 2015. Predictions in neurophysiology. Predict neuronal activity from sensory stimulus/behaviour. “encoding model”. Predict stimulus/behaviour from neuronal activity. “decoding model”. Aayush Mudgal [12008]. Sheallika Singh [12665]. What is Dimensionality Reduction ?. Mapping . of data to lower dimension such . that:. . uninformative variance is . discarded,. . or a subspace where data lives is . John A. Lee, Michel Verleysen. 1. Dimensionality Reduction. By: . sadatnejad. دانشگاه صنعتي اميرکبير. (. پلي تکنيک تهران). Dim. Reduction- . Practical Motivations . 2. 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 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.