2 research outputs found
Jointly Learning Non-negative Projection and Dictionary with Discriminative Graph Constraints for Classification
Sparse coding with dictionary learning (DL) has shown excellent
classification performance. Despite the considerable number of existing works,
how to obtain features on top of which dictionaries can be better learned
remains an open and interesting question. Many current prevailing DL methods
directly adopt well-performing crafted features. While such strategy may
empirically work well, it ignores certain intrinsic relationship between
dictionaries and features. We propose a framework where features and
dictionaries are jointly learned and optimized. The framework, named joint
non-negative projection and dictionary learning (JNPDL), enables interaction
between the input features and the dictionaries. The non-negative projection
leads to discriminative parts-based object features while DL seeks a more
suitable representation. Discriminative graph constraints are further imposed
to simultaneously maximize intra-class compactness and inter-class
separability. Experiments on both image and image set classification show the
excellent performance of JNPDL by outperforming several state-of-the-art
approaches.Comment: To appear in BMVC 201
Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold
Dictionary leaning (DL) and dimensionality reduction (DR) are powerful tools
to analyze high-dimensional noisy signals. This paper presents a proposal of a
novel Riemannian joint dimensionality reduction and dictionary learning
(R-JDRDL) on symmetric positive definite (SPD) manifolds for classification
tasks. The joint learning considers the interaction between dimensionality
reduction and dictionary learning procedures by connecting them into a unified
framework. We exploit a Riemannian optimization framework for solving DL and DR
problems jointly. Finally, we demonstrate that the proposed R-JDRDL outperforms
existing state-of-the-arts algorithms when used for image classification tasks.Comment: European Signal Processing Conference (EUSIPCO 2018