The Johnson-Lindenstrauss Lemma Is Optimal for Linear Dimensionality Reduction

نویسندگان

  • Kasper Green Larsen
  • Jelani Nelson
چکیده

For any n > 1 and 0 < ε < 1/2, we show the existence of an n-point subset X of R such that any linear map from (X, `2) to ` m 2 with distortion at most 1 + ε must have m = Ω(min{n, ε−2 logn}). Our lower bound matches the upper bounds provided by the identity matrix and the Johnson-Lindenstrauss lemma [JL84], improving the previous lower bound of Alon [Alo03] by a log(1/ε) factor.

برای دانلود 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

Geometric Optimization April 12 , 2007 Lecture 25 : Johnson Lindenstrauss Lemma

The topic of this lecture is dimensionality reduction. Many problems have been efficiently solved in low dimensions, but very often the solution to low-dimensional spaces are impractical for high dimensional spaces because either space or running time is exponential in dimension. In order to address the curse of dimensionality, one technique is to map a set of points in a high dimensional space...

متن کامل

Machine Learning Friendly Set Version of Johnson-Lindenstrauss Lemma

In this paper we make a novel use of the Johnson-Lindenstrauss Lemma. The Lemma has an existential form saying that there exists a JL transformation f of the data points into lower dimensional space such that all of them fall into predefined error range δ. We formulate in this paper a theorem stating that we can choose the target dimensionality in a random projection type JL linear transformati...

متن کامل

Using the Johnson-Lindenstrauss lemma in linear and integer programming

The Johnson-Lindenstrauss lemma allows dimension reduction on real vectors with low distortion on their pairwise Euclidean distances. This result is often used in algorithms such as k-means or k nearest neighbours since they only use Euclidean distances, and has sometimes been used in optimization algorithms involving the minimization of Euclidean distances. In this paper we introduce a first a...

متن کامل

Dimension Reduction in the l1 norm

The Johnson-Lindenstrauss Lemma shows that any set of n points in Euclidean space can be mapped linearly down to O((log n)/ǫ) dimensions such that all pairwise distances are distorted by at most 1 + ǫ. We study the following basic question: Does there exist an analogue of the JohnsonLindenstrauss Lemma for the l1 norm? Note that Johnson-Lindenstrauss Lemma gives a linear embedding which is inde...

متن کامل

Lecture 6 : Johnson - Lindenstrauss Lemma : Dimension Reduction

Observer that for any three points, if the three distances between them are given, then the three angles are fixed. Given n−1 vectors, the vectors together with the origin form a set of n points. In fact, given any n points in Euclidean space (in n−1 dimensions), the Johnson-Lindenstrauss Lemma states that the n points can be placed in O( logn 2 ) dimensions such that distances are preserved wi...

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

برای دانلود 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

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2016