2,383 research outputs found
KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization
We consider the image classification problem via kernel collaborative
representation classification with locality constrained dictionary (KCRC-LCD).
Specifically, we propose a kernel collaborative representation classification
(KCRC) approach in which kernel method is used to improve the discrimination
ability of collaborative representation classification (CRC). We then measure
the similarities between the query and atoms in the global dictionary in order
to construct a locality constrained dictionary (LCD) for KCRC. In addition, we
discuss several similarity measure approaches in LCD and further present a
simple yet effective unified similarity measure whose superiority is validated
in experiments. There are several appealing aspects associated with LCD. First,
LCD can be nicely incorporated under the framework of KCRC. The LCD similarity
measure can be kernelized under KCRC, which theoretically links CRC and LCD
under the kernel method. Second, KCRC-LCD becomes more scalable to both the
training set size and the feature dimension. Example shows that KCRC is able to
perfectly classify data with certain distribution, while conventional CRC fails
completely. Comprehensive experiments on many public datasets also show that
KCRC-LCD is a robust discriminative classifier with both excellent performance
and good scalability, being comparable or outperforming many other
state-of-the-art approaches
Similarity Learning via Kernel Preserving Embedding
Data similarity is a key concept in many data-driven applications. Many
algorithms are sensitive to similarity measures. To tackle this fundamental
problem, automatically learning of similarity information from data via
self-expression has been developed and successfully applied in various models,
such as low-rank representation, sparse subspace learning, semi-supervised
learning. However, it just tries to reconstruct the original data and some
valuable information, e.g., the manifold structure, is largely ignored. In this
paper, we argue that it is beneficial to preserve the overall relations when we
extract similarity information. Specifically, we propose a novel similarity
learning framework by minimizing the reconstruction error of kernel matrices,
rather than the reconstruction error of original data adopted by existing work.
Taking the clustering task as an example to evaluate our method, we observe
considerable improvements compared to other state-of-the-art methods. More
importantly, our proposed framework is very general and provides a novel and
fundamental building block for many other similarity-based tasks. Besides, our
proposed kernel preserving opens up a large number of possibilities to embed
high-dimensional data into low-dimensional space.Comment: Published in AAAI 201
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