Consider an m*N matrix Φ with the restricted isometry property of order k and level δ; that is,
the norm of any k-sparse vector in R^N is preserved to within a multiplicative factor of 1±δ …

Compressed sensing (CS) decoding algorithms can efficiently recover an N-dimensional
real-valued vector x to within a factor of its best k-term approximation by taking m= O …

In this paper we show that for the purposes of dimensionality reduction certain class of
structured random matrices behave similarly to random Gaussian matrices. This class …

Johnson--Lindenstrauss Transforms are powerful tools for reducing the dimensionality of
data while preserving key characteristics of that data, and they have found use in many …

Abstract The Johnson-Lindenstaruss lemma (Johnson & Lindenstrauss, 1984) is a
cornerstone result in dimensionality reduction, stating it is possible to embed a set of $ n …

Compressed Sensing (CS) is a novel data acquisition theorem exploiting the signals
sparsity differing from traditional Nyquist theorem in the ability of obtaining all information of …

This paper addresses the problem of correlation estimation in sets of compressed images.
We consider a framework where the images are represented under the form of linear …

Abstract The Communist Party of China proposed the concept of “speeding up the industrial
transformation and changing economic development mode” in its 17 th Congress. Against …

In this work, we applied several techniques for data reduction to publicly available datasets
with the goal of comparing how an increasing level of compression affects the performance …

To achieve a good trade-off between recognition accuracy and computational efficiency, it is
often needed to reduce high-dimensional visual data to medium-dimensional ones. For this …