CPS 290 : Algorithmic Foundations of Data Science February 3 , 2017 Lecture 6 : Dimensionality Reduction

نویسنده

  • Kamesh Munagala
چکیده

which simply counts the number of coordinates where the points differ. Consider now the following simple hash family: H = {hk | hk(~x) = k bit of ~x} Such a hash function maps each item to one of two buckets. Let Zij denote the random variable that is d if items xi and xj map to different buckets and 0 otherwise, where the randomness is over the hash function chosen from H. Then it is easy to check that

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Principles of Data Analytics

These are abridged lecture notes from the Spring 2017 course offering of “Principles of Data Analytics” that I offer at Iowa State University annually. This graduate level course offers an introduction to a variety of data analysis techniques, particularly those relevant for electrical and computer engineers, from an algorithmic perspective. Topics include techniques for classification, visuali...

متن کامل

Math 140a: Foundations of Real Analysis I

1. Ordered Sets, Ordered Fields, and Completeness 1 1.1. Lecture 1: January 5, 2016 1 1.2. Lecture 2: January 7, 2016 4 1.3. Lecture 3: January 11, 2016 7 1.4. Lecture 4: January 14, 2014 9 2. Sequences and Limits 13 2.1. Lecture 5: January 19, 2016 13 2.2. Lecture 6: January 21, 2016 15 2.3. Lecture 7: January 26, 2016 18 2.4. Lecture 8: January 28, 2016 21 3. Extensions of R: the Extended Rea...

متن کامل

Tenenbaum , Dimensionality Reduction A Global Geometric Framework for Nonlinear

www.sciencemag.org (this information is current as of February 19, 2007 ): The following resources related to this article are available online at http://www.sciencemag.org/cgi/content/full/290/5500/2319 version of this article at: including high-resolution figures, can be found in the online Updated information and services, http://www.sciencemag.org/cgi/content/full/290/5500/2319/DC1 can be f...

متن کامل

Lecture 5 – February 17 , 2016

This lecture is on dimensionality reduction, which aims at ‘squashing’ down the dimensionality while still preserving some geometric properties. The motivation behind this technique is that many types of data are high-dimensional, and it will be operationally much easier to manipulate these data in a lower dimension. For instance, the bag-of-words representation of documents, which treats every...

متن کامل

Lawrence K . Saul Nonlinear Dimensionality Reduction by Locally Linear Embedding

, 2323 (2000); 290 Science Sam T. Roweis and Lawrence K. Saul Nonlinear Dimensionality Reduction by Locally Linear Embedding This copy is for your personal, non-commercial use only. clicking here. colleagues, clients, or customers by , you can order high-quality copies for your If you wish to distribute this article to others here. following the guidelines can be obtained by Permission to repu...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017