About this Research Topic
Low-rank priors, a fundamental assumption for dimensionality reduction, have attracted a lot of attention in the last decade. They have wide applications in machine learning, data analysis, signal/image/video processing, bioinformatics, and many other fields. To obtain quality solutions from low-rank models, optimization techniques have played a crucial role in the relevant studies.
In the era of big data, many applications generate much larger datasets, and this motivates techniques to process and analyze data. Thus, low-rank priors, an important element of dimension reduction methods, are highly relevant. However, larger datasets bring new challenges to the research community. In particular, there is a need for scalable models and/or efficient optimization methods to address large-scale noisy low-rank datasets in an accurate and timely manner.
Topics of interest include, but are not limited to:
- Principles in low-rank modeling
- Analysis of low-rank priors under various bases
- Novel sampling methods with low-rank priors
- Efficient algorithms for low-rank problems
- Applications related to low-rank priors
In the era of big data, many applications generate much larger datasets, and this motivates techniques to process and analyze data. Thus, low-rank priors, an important element of dimension reduction methods, are highly relevant. However, larger datasets bring new challenges to the research community. In particular, there is a need for scalable models and/or efficient optimization methods to address large-scale noisy low-rank datasets in an accurate and timely manner.
Topics of interest include, but are not limited to:
- Principles in low-rank modeling
- Analysis of low-rank priors under various bases
- Novel sampling methods with low-rank priors
- Efficient algorithms for low-rank problems
- Applications related to low-rank priors
Keywords: matrix factorization, tensor factorization, dimensionality reduction, matrix completion, data analytics
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