When compressive learning fails: blame the decoder or the sketch?

Abstract: In compressive learning, a mixture model (a set of centroids or a Gaussian mixture) is learned from a sketch vector, that serves as a highly compressed representation of the dataset.

An Analog-to-Information VGA Image Sensor Architecture for Support Vector Machine on Compressive Measurements

Abstract: This work presents a compact VGA (480 × 640) CMOS Image Sensor (CIS) architecture with dedicated end-of-column Compressive Sensing (CS) scheme allowing embedded object recognition. The architecture takes advantage of a low-footprint pseudo-random data mixing circuit and a first order incremental Sigma-Delta (ΣΔ) Analog to Digital Converter (ADC) to extract compressed features.

Compressive k-Means with Differential Privacy

Abstract: In the compressive learning framework, one harshly compresses a whole training dataset into a single vector of generalized random moments, the sketch, from which a learning task can subsequently be performed.

Compressive Single-pixel Fourier Transform Imaging using Structured Illumination

Abstract: Single Pixel (SP) imaging is now a reality in many applications, eg, biomedical ultrathin endoscope and fluorescent spectroscopy. In this context, many schemes exist to improve the light throughput of these device, eg, using structured illumination driven by compressive sensing theory.

Differentially Private Compressive K-means

Abstract: This work addresses the problem of learning from large collections of data with privacy guarantees. The sketched learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, from which the learning task is then performed.

Exploring Hierarchical Machine Learning for Hardware-Limited Multi-Class Inference on Compressed Measurements

Abstract: This paper explores hierarchical clustering methods to learn a hierarchical multi-class classifier on compressed measurements in the context of highly constrained hardware (e.g., always-on ultra low power vision systems). In contrast to the popular multi-class classification approaches based on multiple binary classifiers (i.

Iterative Low-rank and rotating sparsity promotion for circumstellar disks imaging

Abstract: Recent astronomical observations open the possibility to directly image circumstellar disks, a key feature for our understanding of extra-solar systems. However, the faint intensity of these celestial signals compared to the brightness of their hosting star make their accurate characterization a challenging processing task.

Near Sensor Decision Making via Compressed Measurements for Highly Constrained Hardware

Abstract: This work presents and compare three realistic scenarios to perform near sensor decision making based on Dimensionality Reduction (DR) techniques of high dimensional signals in the context of highly constrained hardware.

One-Bit Sensing of Low-Rank and Bisparse Matrices

Abstract: This note studies the worst-case recovery error of low-rank and bisparse matrices as a function of the number of one-bit measurements used to acquire them. First, by way of the concept of consistency width, precise estimates are given on how fast the recovery error can in theory decay.

Performance of Compressive Sensing for Hadamard-Haar Systems

Abstract: We study the problem of image recovery from subsampled Hadamard measurements using Haar wavelet sparsity prior. This problem is of interest in, e.g., computational imaging applications relying on optical multiplexing or single pixel imaging.