7 citations · 7 across the 5 of their papers we have counts for
5 papers
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring
Sibylle Hess, Wouter Duivesteijn, Decebal Mocanu
We formally prove the connection between k-means clustering and the predictions of neural networks based on the softmax activation layer. In existing work, this connection has been…
The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization
Sibylle Hess, Nico Piatkowski, Katharina Morik
Boolean matrix factorization (BMF) is a popular and powerful technique for inferring knowledge from data. The mining result is the Boolean product of two matrices, approximating th…
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering
Sibylle Hess, Wouter Duivesteijn, Philipp Honysz +1
When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporatin…
C-SALT: Mining Class-Specific ALTerations in Boolean Matrix Factorization
Sibylle Hess, Katharina Morik
Given labeled data represented by a binary matrix, we consider the task to derive a Boolean matrix factorization which identifies commonalities and specifications among the classes…
The PRIMPing Routine -- Tiling through Proximal Alternating Linearized Minimization
Sibylle Hess, Katharina Morik, Nico Piatkowski
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 f…