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 . 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 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 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 . local. image . descriptors. . into. . compact. . codes. Authors. :. Hervé. . Jegou. Florent. . Perroonnin. Matthijs. . Douze. Jorge. . Sánchez. Patrick . Pérez. Cordelia. Schmidt. Presented. 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. 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. 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. Devansh Arpit. Motivation. Abundance of data. Required storage space explodes!. Images. Documents. Videos. Motivation. Speedup Algorithms. Motivation. Dimensionality reduction for noise filtering. Vector Representation. 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. John A. Lee, Michel Verleysen, . Chapter4 . 1. Distance Preservation. دانشگاه صنعتي اميرکبير. (. پلي تکنيک تهران). 2. The motivation behind distance preservation is that any . 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 Md. . . Sujan. . Ali. Associate Professor. Dept. of Computer Science and Engineering. Jatiya. . Kabi. . Kazi. . Nazrul. Islam University. Dimensionality Reduction and Classification. V. ariance.