# 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} }

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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## Optimal Analysis of Subset-Selection Based L_p Low Rank Approximation

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## L G ] 3 0 O ct 2 01 9 Optimal Analysis of Subset-Selection Based l p Low-Rank Approximation

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#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 48 REFERENCES

## Mining discrete patterns via binary matrix factorization

VIEW 2 EXCERPTS

## Relative-Error CUR Matrix Decompositions

VIEW 2 EXCERPTS

## Column subset selection via sparse approximation of SVD

VIEW 1 EXCERPT

## Adaptive Sampling and Fast Low-Rank Matrix Approximation

VIEW 2 EXCERPTS

## Efficient Volume Sampling for Row/Column Subset Selection

VIEW 2 EXCERPTS