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Expectile Matrix Factorization for Skewed Data Analysis
Matrix factorization is a popular approach to solving matrix estimation ...
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From-Below Boolean Matrix Factorization Algorithm Based on MDL
During the past few years Boolean matrix factorization (BMF) has become ...
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The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization
Boolean matrix factorization (BMF) is a popular and powerful technique f...
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C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization
Given labeled data represented by a binary matrix, we consider the task ...
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Complex Matrix Factorization for Face Recognition
This work developed novel complex matrix factorization methods for face ...
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A description length approach to determining the number of k-means clusters
We present an asymptotic criterion to determine the optimal number of cl...
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The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization
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 in the presence of 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. Thereby, no convexity assumptions have to be met. 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.
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