8 citations · 27 across the 10 of their papers we have counts for
11 papers
Bootstrap Deep Spectral Clustering with Optimal Transport
Wengang Guo, Wei Ye, Chunchun Chen +4
Spectral clustering is a leading clustering method. Two of its major shortcomings are the disjoint optimization process and the limited representation capacity. To address these is…
An Introductory Survey to Autoencoder-based Deep Clustering -- Sandboxes for Combining Clustering with Deep Learning
Collin Leiber, Lukas Miklautz, Claudia Plant +1
Autoencoders offer a general way of learning low-dimensional, non-linear representations from data without labels. This is achieved without making any particular assumptions about…
SHADE: Deep Density-based Clustering
Anna Beer, Pascal Weber, Lukas Miklautz +4
Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analys…
Extension of the Dip-test Repertoire -- Efficient and Differentiable p-value Calculation for Clustering
Lena G. M. Bauer, Collin Leiber, Christian Böhm +1
Over the last decade, the Dip-test of unimodality has gained increasing interest in the data mining community as it is a parameter-free statistical test that reliably rates the mod…
Automatic Parameter Selection for Non-Redundant Clustering
Collin Leiber, Dominik Mautz, Claudia Plant +1
High-dimensional datasets often contain multiple meaningful clusterings in different subspaces. For example, objects can be clustered either by color, weight, or size, revealing di…
Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores
Fiete Lüer, Tobias Weber, Maxim Dolgich +1
Anomaly detection in imbalanced datasets is a frequent and crucial problem, especially in the medical domain where retrieving and labeling irregularities is often expensive. By com…
An Interpretable Neuron Embedding for Static Knowledge Distillation
Wei Han, Yangqiming Wang, Christian Böhm +1
Although deep neural networks have shown well-performance in various tasks, the poor interpretability of the models is always criticized. In the paper, we propose a new interpretab…
Deep Clustering With Consensus Representations
Lukas Miklautz, Martin Teuffenbach, Pascal Weber +4
The field of deep clustering combines deep learning and clustering to learn representations that improve both the learned representation and the performance of the considered clust…
Network Embedding via Deep Prediction Model
Xin Sun, Zenghui Song, Yongbo Yu +3
Network-structured data becomes ubiquitous in daily life and is growing at a rapid pace. It presents great challenges to feature engineering due to the high non-linearity and spars…
Massively Parallel Graph Drawing and Representation Learning
Christian Böhm, Claudia Plant
To fully exploit the performance potential of modern multi-core processors, machine learning and data mining algorithms for big data must be parallelized in multiple ways. Today's…
Space-filling Curves for High-performance Data Mining
Christian Böhm
Space-filling curves like the Hilbert-curve, Peano-curve and Z-order map natural or real numbers from a two or higher dimensional space to a one dimensional space preserving locali…