'Reduce dimensionality' presentation slideshows

Line Features

CS 636 Computer Vision. Line Features. Nathan Jacobs. review. What properties should feature points have? discriminant easy to localize robust to viewpoint/photometric changes What local properties can you use to find them? all the same thing: second moment matrix, structure tensor, .

991 views • 90 slides

Dimensionality reduction PCA, SVD, MDS, ICA, and friends

Dimensionality reduction PCA, SVD, MDS, ICA, and friends. Jure Leskovec Machine Learning recitation April 27 2006. Why dimensionality reduction?. Some features may be irrelevant We want to visualize high dimensional data

1.51k views • 50 slides

Principle Component Analysis

Principle Component Analysis. Dr Poonam Goyal CS & IS BITS, Pilani. Introduction. how do we get from this data set to a simple equation of x ?. Introduction. How can we identify the most meaningful basis to re-express a data set

529 views • 37 slides

Self Organization of a Massive Document Collection

Self Organization of a Massive Document Collection. Teuvo Kohonen Samuel Kaski Krista Lagus Jarkko Salojarvi Jukka Honkela Vesa Paatero Antti Saarela. Their Goal. Organize large document collections according to textual similarities Search engine

561 views • 31 slides

Dimensionality Reduction in Hyperspectral Image Analysis Using Independent Component Analysis

Dimensionality Reduction in Hyperspectral Image Analysis Using Independent Component Analysis. Hongtao Du Feb 18, 2003. Status Summary. What is hyperspectral image? How to analyze hyperspectral image? Why reduce dimensionality in hyperspectral image analysis?

512 views • 19 slides

COMPE 467 - Pattern Recognition

Dimensionality Reduction. COMPE 467 - Pattern Recognition. Dimensionality Reduction. We can reduce dimensionality by combining features . Principle Component Analysis seeks a projection that best represents the data in a least square sense. 1. Principal Component Analysis. 1.

338 views • 18 slides

Generic Object Recognition

Generic Object Recognition. A Project on. -- by Yatharth Saraf. Problem Definition and Background. Recognizing generic class or category of a given object as opposed to recognizing specific, individual objects

400 views • 17 slides

Locally Linear Embedding and Topology Representing Networks

183 views • 6 slides