Abstract
Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems. Most of the existing DL methods aim to learn a synthesis dictionary to represent the input signal while enforcing the representation coefficients and/or representation residual to be discriminative. However, the ℓ0 or ℓ1-norm sparsity constraint on the representation coefficients adopted in most DL methods makes the training and testing phases time consuming. We propose a new discriminative DL framework, namely projective dictionary pair learning (DPL), which learns a synthesis dictionary and an analysis dictionary jointly to achieve the goal of signal representation and discrimination. Compared with conventional DL methods, the proposed DPL method can not only greatly reduce the time complexity in the training and testing phases, but also lead to very competitive accuracies in a variety of visual classification tasks.
Original language | English |
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Pages (from-to) | 793-801 |
Number of pages | 9 |
Journal | Advances in Neural Information Processing Systems |
Volume | 1 |
Issue number | January |
Publication status | Published - 1 Jan 2014 |
Event | 28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada Duration: 8 Dec 2014 → 13 Dec 2014 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing