'Linear dimensionality reduction' presentation slideshows

Principal Component Analysis

1.17k views • 116 slides

2007 NIPS Tutorial on: Deep Belief Nets

2007 NIPS Tutorial on: Deep Belief Nets. Geoffrey Hinton Canadian Institute for Advanced Research & Department of Computer Science University of Toronto. Some things you will learn in this tutorial.

1.62k views • 100 slides

2007 NIPS Tutorial on: Deep Belief Nets

2007 NIPS Tutorial on: Deep Belief Nets. Geoffrey Hinton Canadian Institute for Advanced Research & Department of Computer Science University of Toronto. Some things you will learn in this tutorial.

1.15k views • 100 slides

Spectral Hashing

Spectral Hashing. Y. Weiss (Hebrew U.) A. Torralba (MIT) Rob Fergus (NYU). What does the world look like?. Motivation. High level image statistics. Object Recognition for large-scale search . Semantic Hashing. [Salakhutdinov & Hinton, 2007]. Query Image. Semantic Hash Function.

1.95k views • 41 slides

3D Geometry for Computer Graphics

3D Geometry for Computer Graphics. Class 7. The plan today. Review of SVD basics Connection PCA Applications: Eigenfaces Animation compression. The geometry of linear transformations. A. A linear transform A always takes hyper-spheres to hyper-ellipses. A.

376 views • 36 slides

The Next Big One : Detecting Earthquakes and Other Rare Events from Community Sensors

The Next Big One : Detecting Earthquakes and Other Rare Events from Community Sensors. Matthew Faulkner , Michael Olson, Rishi Chandy , Jonathan Krause, Mani Chandy , Andreas Krause. Community-Based Sense & Response.

518 views • 32 slides

CSC321 Introduction to Neural Networks and Machine Learning Lecture 21 Using Boltzmann machines to initialize backpropag

CSC321 Introduction to Neural Networks and Machine Learning Lecture 21 Using Boltzmann machines to initialize backpropagation . Geoffrey Hinton. Some problems with backpropagation.

417 views • 27 slides

Diffusion Maps and Spectral Clustering

1 /14. Machine Learning Seminar Series. Diffusion Maps and Spectral Clustering. Author : Ronald R. Coifman et al. (Yale University) Presenter : Nilanjan Dasgupta (SIG Inc.). Z. Y. X. 2 /14. Motivation. -- Datum. Low-dimensional Manifold.

681 views • 14 slides

Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction

Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction. Masashi Sugiyama. Presented by Xianwang Wang . Dimensionality Reduction. Goal Embed high-dimensional data to low-dimensional space Preserve intrinsic information Example. High dimension. 3-dimension. Categories.

232 views • 14 slides

Kernel Case: Caltech 101

Linear Dimensionality Reduction Using the Sparse Linear Model. Ioannis Gkioulekas and Todd Zickler. Harvard School of Engineering and Applied Sciences. Unsupervised Linear Dimensionality Reduction. Locality Preserving Projections: preserve local distances.

277 views • 1 slides