Low Rank Approximation of Binary Matrices: Column Subset Selection and Generalizations

@inproceedings{Dan2018LowRA,
  title={Low Rank Approximation of Binary Matrices: Column Subset Selection and Generalizations},
  author={Chen Dan and Kristoffer Arnsfelt Hansen and He Jiang and Liwei Wang and Yuchen Zhou},
  booktitle={MFCS},
  year={2018}
}
  • Chen Dan, Kristoffer Arnsfelt Hansen, +2 authors Yuchen Zhou
  • Published in MFCS 2018
  • Mathematics, Computer Science
  • Low rank matrix approximation is an important tool in machine learning. Given a data matrix, low rank approximation helps to find factors, patterns and provides concise representations for the data. Research on low rank approximation usually focus on real matrices. However, in many applications data are binary (categorical) rather than continuous. This leads to the problem of low rank approximation of binary matrix. Here we are given a $d \times n$ binary matrix $A$ and a small integer $k$. The… CONTINUE READING

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