6,721 research outputs found
Analysis Dictionary Learning: An Efficient and Discriminative Solution
Discriminative Dictionary Learning (DL) methods have been widely advocated
for image classification problems. To further sharpen their discriminative
capabilities, most state-of-the-art DL methods have additional constraints
included in the learning stages. These various constraints, however, lead to
additional computational complexity. We hence propose an efficient
Discriminative Convolutional Analysis Dictionary Learning (DCADL) method, as a
lower cost Discriminative DL framework, to both characterize the image
structures and refine the interclass structure representations. The proposed
DCADL jointly learns a convolutional analysis dictionary and a universal
classifier, while greatly reducing the time complexity in both training and
testing phases, and achieving a competitive accuracy, thus demonstrating great
performance in many experiments with standard databases.Comment: ICASSP 201
Learning Sparse Adversarial Dictionaries For Multi-Class Audio Classification
Audio events are quite often overlapping in nature, and more prone to noise
than visual signals. There has been increasing evidence for the superior
performance of representations learned using sparse dictionaries for
applications like audio denoising and speech enhancement. This paper
concentrates on modifying the traditional reconstructive dictionary learning
algorithms, by incorporating a discriminative term into the objective function
in order to learn class-specific adversarial dictionaries that are good at
representing samples of their own class at the same time poor at representing
samples belonging to any other class. We quantitatively demonstrate the
effectiveness of our learned dictionaries as a stand-alone solution for both
binary as well as multi-class audio classification problems.Comment: Accepted in Asian Conference of Pattern Recognition (ACPR-2017
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