Abstract
This paper presents a matrix factorization method for dimensionality reduction, semi-supervised two-way multimodal online matrix factorization (STWOMF). This method performs a semantic embedding by finding a linear mapping to a low dimensional semantic space modeled by the original high dimensional feature representation and the label space. An important characteristic of the proposed algorithm is that the new representation can be learned in a semi-supervised fashion. So, annotated instances are used to maximize the discrimination between classes, but also, non-annotated instances can be exploited to estimate the intrinsic manifold structure of the data. Another important advantage of this algorithm is its online formulation that allows to deal with large-scale collections by keeping low computational requirements. According with the experimental evaluation, the proposed STWOMF in comparison with several linear supervised, unsupervised and semi-supervised dimensionality reduction methods, presents a competitive performance in classification while having a lower computational cost.
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Keywords
- Dimensionality Reduction
- Independent Component Analysis
- Semantic Space
- Locality Preserve Projection
- Dimensionality Reduction Method
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Beltrán, V., Vanegas, J.A., González, F.A. (2015). Semi-supervised Dimensionality Reduction via Multimodal Matrix Factorization. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_81
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DOI: https://doi.org/10.1007/978-3-319-25751-8_81
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