DMCA
Multi-Label Transfer Learning with Sparse Representation
| Citations: | 2 - 0 self |
BibTeX
@MISC{Han_multi-labeltransfer,
author = {Yahong Han and Fei Wu and Yueting Zhuang and Xiaofei He},
title = {Multi-Label Transfer Learning with Sparse Representation},
year = {}
}
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Abstract
Abstract—Due to the visually polysemous barrier, videos and images may be annotated by multiple tags. Discovering the correlations among different tags can significantly help predicting precise labels for videos and images. Many of recent studies toward multi-label learning construct a linear subspace embedding with encoded multi-label information, such that data points sharing many common labels tend to be close to each other in the embedded subspace. Motivated by the advances of compressive sensing research, a sparse representation that selects a compact subset to describe the input data can be more discriminative. In this paper, we propose a sparse multi-label learning method to circumvent the visually polysemous barrier of multiple tags. Our approach learns a multi-label encoded sparse linear embedding space from a related dataset, and maps the target data into the learned new representation space to achieve better annotation performance. Instead of using l1-norm penalty (lasso) to induce sparse representation, we propose to formulate the multi-label learning as a penalized least squares optimization problem with elastic-net penalty. By casting the video concept detection and image annotation tasks into a sparse multi-label transfer learning framework in this paper, ridge regression, lasso, elastic net, and the multi-label extended sparse discriminant analysis methods are, respectively, well explored and compared. Index Terms—Image annotation, multi-label learning, sparse representation, transfer learning, video concept detection. I.
Keyphrases
sparse representation multi-label transfer learning multiple tag video concept detection polysemous barrier multi-label learning input data image annotation task index term image annotation abstract due elastic net elastic-net penalty sparse discriminant analysis method linear subspace sparse multi-label transfer l1-norm penalty encoded multi-label information penalized least square learned new representation space different tag target data annotation performance recent study many common label precise label ridge regression compact subset transfer learning data point related dataset sparse multi-label multi-label encoded sparse embedded subspace
