Confident Kernel Sparse Coding and Dictionary Learning

In recent years, kernel-based sparse coding (K-SRC) has received a special attention due to its efficient representation of nonlinear data structures in the feature space. Nevertheless, the existing K-SRC methods suffer from the lack of consistency between their training and test optimization framew...

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Bibliographic Details
Published in:Proceedings (IEEE International Conference on Data Mining) pp. 1031 - 1036
Main Authors: Hosseini, Babak, Hammer, Barbara
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2018
Subjects:
Dictionaries
Discriminative dictionary learning
Encoding
Kernel
Kernel sparse coding
Machine learning
Non-negative reconstruction
Optimization
Sparse matrices
Training
ISSN:2374-8486
Online Access:Get full text
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Summary:In recent years, kernel-based sparse coding (K-SRC) has received a special attention due to its efficient representation of nonlinear data structures in the feature space. Nevertheless, the existing K-SRC methods suffer from the lack of consistency between their training and test optimization frameworks. In this work, we propose a novel confident K-SRC and dictionary learning algorithm (CKSC) which focuses on the discriminative reconstruction of the data based on its representation in the kernel space. CKSC focuses on reconstructing each data sample via weighted contributions which are confident in its corresponding class of data. We employ novel discriminative terms to apply this scheme to both training and test frameworks in our algorithm. This increases the consistency of these optimization frameworks and improves the discriminative performance in the recall phase. In addition, CKSC directly employs the supervised information in its dictionary learning framework to enhance the discriminative structure of the dictionary. For empirical evaluations, we implement our CKSC algorithm on multivariate time-series benchmarks such as DynTex++ and UTKinect. Our claims regarding the superior performance of the proposed algorithm are justified throughout comparing its classification results to the state-of-the-art K-SRC algorithms.
ISSN:2374-8486
DOI:10.1109/ICDM.2018.00130