A multi-class structured dictionary learning method using discriminant atom selection

12/04/2018
by   R. E. Rolón, et al.
0

In the last decade, traditional dictionary learning methods have been successfully applied to various pattern classification tasks. Although these methods produce sparse representations of signals which are robust against distortions and missing data, such representations quite often turn out to be unsuitable if the final objective is signal classification. In order to overcome or at least to attenuate such a weakness, several new methods which incorporate discriminative information into sparse-inducing models have emerged in recent years. In particular, methods for discriminative dictionary learning have shown to be more accurate (in terms of signal classification) than the traditional ones, which are only focused on minimizing the total representation error. In this work, we present both a novel multi-class discriminative measure and an innovative dictionary learning method. For a given dictionary, this new measure, which takes into account not only when a particular atom is used for representing signals coming from a certain class and the magnitude of its corresponding representation coefficient, but also the effect that such an atom has in the total representation error, is capable of efficiently quantifying the degree of discriminability of each one of the atoms. On the other hand, the new dictionary construction method yields dictionaries which are highly suitable for multi-class classification tasks. Our method was tested with a widely used database for handwritten digit recognition and compared with three state-of-the-art classification methods. The results show that our method significantly outperforms the other three achieving good recognition rates and additionally, reducing the computational cost of the classifier.

READ FULL TEXT

page 9

page 17

research
09/19/2014

Active Dictionary Learning in Sparse Representation Based Classification

Sparse representation, which uses dictionary atoms to reconstruct input ...
research
05/14/2020

Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification

Dictionary learning for sparse representations has been successful in ma...
research
11/17/2021

Discriminative Dictionary Learning based on Statistical Methods

Sparse Representation (SR) of signals or data has a well founded theory ...
research
07/14/2022

Deep Dictionary Learning with An Intra-class Constraint

In recent years, deep dictionary learning (DDL)has attracted a great amo...
research
12/12/2018

An efficient supervised dictionary learning method for audio signal recognition

Machine hearing or listening represents an emerging area. Conventional a...
research
03/26/2020

Classification of the Chinese Handwritten Numbers with Supervised Projective Dictionary Pair Learning

Image classification has become a key ingredient in the field of compute...
research
07/13/2018

Analysis Dictionary Learning based Classification: Structure for Robustness

A discriminative structured analysis dictionary is proposed for the clas...

Please sign up or login with your details

Forgot password? Click here to reset