Efficient Dictionary Learning via Very Sparse Random Projections

2015-04-05 · Farhad Pourkamali-Anaraki, Stephen Becker, Shannon M. Hughes

Performing signal processing tasks on compressive measurements of data has received great attention in recent years. In this paper, we extend previous work on compressive dictionary learning by showing that more general random projections may be used, including sparse ones. More precisely, we examine compressive K-means clustering as a special case of compressive dictionary learning and give theoretical guarantees for its performance for a very general class of random projections. We then propose a memory and computation efficient dictionary learning algorithm, specifically designed for analyzing large volumes of high-dimensional data, which learns the dictionary from very sparse random projections. Experimental results demonstrate that our approach allows for reduction of computational complexity and memory/data access, with controllable loss in accuracy.

📄 PDF Abstract BibTeX arXiv:1504.01169

Code (0)

등록된 구현이 없습니다.

Tasks

ClusteringDictionary Learning

Methods 이 논문이 사용한 방법론

k-Means Clustering k-Means Clustering is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$…

Optimal Projections for Discriminative Dictionary Learning using the JL-lemma

2023-08-27 · G. Madhuri, Atul Negi, Kaluri V. Rangarao

Dimensionality reduction-based dictionary learning methods in the literature have often used iterative random projections. The dimensionality of such a random projection matrix is a random number that might not lead to a…

Dictionary Learning Dimensionality Reduction Face Recognition LEMMA+1

Dictionary Learning Based on Sparse Distribution Tomography

2017-08-01 · ICML 2017 8 · Pedram Pad, Farnood Salehi, Elisa Celis, Patrick Thiran 외

We propose a new statistical dictionary learning algorithm for sparse signals that is based on an $\alpha$-stable innovation model. The parameters of the underlying model—that is, the atoms of the dictionary, the sp…

Denoising Dictionary Learning Image Denoising

Dictionary and Image Recovery from Incomplete and Random Measurements

2015-08-02 · Mohammad Aghagolzadeh, Hayder Radha

This paper tackles algorithmic and theoretical aspects of dictionary learning from incomplete and random block-wise image measurements and the performance of the adaptive dictionary for sparse image recovery. This proble…

compressed sensing Dictionary Learning Diversity

Efficient Dictionary Learning with Sparseness-Enforcing Projections

2016-04-16 · Markus Thom, Matthias Rapp, Günther Palm

Learning dictionaries suitable for sparse coding instead of using engineered bases has proven effective in a variety of image processing tasks. This paper studies the optimization of dictionaries on image data where the …

Denoising Dictionary Learning

Robust method for finding sparse solutions to linear inverse problems using an L2 regularization

2017-01-03 · Gonzalo H Otazu

We analyzed the performance of a biologically inspired algorithm called the Corrected Projections Algorithm (CPA) when a sparseness constraint is required to unambiguously reconstruct an observed signal using atoms from …

L2 Regularization