PRIMPing under the PALM
Sibylle Hess, Nico Piatkowski, Katharina Morik
Mining and exploring databases should provide users with knowledge and new insights.
Tiles of data strive to unveil true underlying structure and distinguish valuable information from various kinds of noise.
We propose a novel Boolean matrix factorization algorithm to solve the tiling problem, based on recent results from optimization theory.
In contrast to existing work, the new algorithm minimizes the description length of the resulting factorization. This approach is well-known for model
selection and data compression, but not for finding suitable factorizations via numerical optimization.
We demonstrate the superior robustness of the new approach against several kinds of noise and types of underlying structure.
Moreover, our general framework can work with any cost-measure having a suitable real-valued relaxation.
The experimental results on synthetic data and image data show that the new method identifies interpretable patterns
which explain the data almost always better than the competing algorithms.
Requirements:
- 64 Bit Linux
- CUDA 7.5
- libjpeg (for JPEG to CSV conversion)
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