PPT-Dimensionality Reduction: Principal Component

Analysis CS771 Introduction to Machine Learning Nisheeth Kmeans loss function recap 2 X Z   N K K       denotes a length onehot encoding of   Remember the matrix factorization view of the kmeans loss function. Computer Vision. Face Recognition Using Principal Components . Analysis (PCA). M. Turk, A. . Pentland. , ". Eigenfaces. for Recognition. ", Journal of Cognitive Neuroscience, 3(1), pp. 71-86, 1991. . 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. 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 . 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. 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. 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. 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. 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, . 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 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.