Search Results for author:
Found 8 papers, 3 papers with code
Automatic Parameter Selection for Non-Redundant Clustering
In this paper, we propose a framework that utilizes the Minimum Description Length Principle (MDL) to detect the number of subspaces and clusters per subspace automatically.
Extension of the Dip-test Repertoire -- Efficient and Differentiable p-value Calculation for Clustering
This accelerates computation and provides an approximation of the Dip- to Dip-p-value transformation for every single sample size.
Interpretable Subgraph Feature Extraction for Hyperlink Prediction
In this study, we present SSF, an innovative hyperlink prediction methodology based on Subgraph Structural Features.
Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores
Anomaly detection in imbalanced datasets is a frequent and crucial problem, especially in the medical domain where retrieving and labeling irregularities is often expensive.
An Interpretable Neuron Embedding for Static Knowledge Distillation
The visualization of semantic vectors allows for a qualitative explanation of the neural network.
Deep Clustering With Consensus Representations
Further, we propose DECCS (Deep Embedded Clustering with Consensus representationS), a deep consensus clustering method that learns a consensus representation by enhancing the embedded space to such a degree that all ensemble members agree on a common clustering result.
Massively Parallel Graph Drawing and Representation Learning
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.
Space-filling Curves for High-performance Data Mining
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 locality.
