Denoising individual bias for a fairer binary submatrix detection
This addresses fairness and accuracy issues in binary data analysis for applications like recommendation systems or bioinformatics, but it is incremental as it builds on existing BMF and CC methods.
The paper tackles the problem of binary matrix factorization and co-clustering methods being hindered by heterogeneous background noise in real data, proposing BIND to denoise by estimating row- or column-wise mixture distributions, which drastically increases fairness and accuracy in experiments.
Low rank representation of binary matrix is powerful in disentangling sparse individual-attribute associations, and has received wide applications. Existing binary matrix factorization (BMF) or co-clustering (CC) methods often assume i.i.d background noise. However, this assumption could be easily violated in real data, where heterogeneous row- or column-wise probability of binary entries results in disparate element-wise background distribution, and paralyzes the rationality of existing methods. We propose a binary data denoising framework, namely BIND, which optimizes the detection of true patterns by estimating the row- or column-wise mixture distribution of patterns and disparate background, and eliminating the binary attributes that are more likely from the background. BIND is supported by thoroughly derived mathematical property of the row- and column-wise mixture distributions. Our experiment on synthetic and real-world data demonstrated BIND effectively removes background noise and drastically increases the fairness and accuracy of state-of-the arts BMF and CC methods.