Randomness Efficient Fast-Johnson-Lindenstrauss Transform with Applications in Differential Privacy and Compressed Sensing

@inproceedings{Upadhyay2014RandomnessEF,
  title={Randomness Efficient Fast-Johnson-Lindenstrauss Transform with Applications in Differential Privacy and Compressed Sensing},
  author={Jalaj Upadhyay},
  year={2014}
}
The Johnson-Lindenstrauss property (JLP) of random matrices has immense applications in computer science ranging from compressed sensing, learning theory, numerical linear algebra, to privacy. This paper explores the properties and applications of a distribution of random matrices. Our distribution satisfies JLP with desirable properties like fast matrix-vector multiplication, bounded sparsity, and optimal subspace embedding. We can sample a random matrix from this distribution using exactly 2n… CONTINUE READING

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