PPT-Dimensionality Reduction
Author : tatyana-admore | Published Date : 2016-03-22
Computer Graphics Course June 2013 What is high dimensional data Images Videos Documents Most data actually What is high dimensional data Images dimension 3XY Videos
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Dimensionality Reduction: Transcript
Computer Graphics Course June 2013 What is high dimensional data Images Videos Documents Most data actually What is high dimensional data Images dimension 3XY Videos dimension of image number of frames. 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 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. 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. 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”. 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. 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 pre-processing . 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
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