Christian Böhm

NeurIPS Conference 2025 Conference Paper

Anomaly Detection by an Ensemble of Random Pairs of Hyperspheres

• Walid Durani
• Collin Leiber
• Khalid Durani
• Claudia Plant
• Christian Böhm

Anomaly detection is a crucial task in data mining, focusing on identifying data points that deviate significantly from the main patterns in the data. This paper introduces Anomaly Detection by an Ensemble of Random Pairs of Hyperspheres (ADERH), a new isolation-based technique leveraging two key observations: (i) anomalies are comparatively rare, and (ii) they typically deviate more strongly from general patterns than normal data points. Drawing on a delta-separation argument, ADERH constructs an ensemble of multi-scale hyperspheres built upon randomly paired data points to identify anomalies. To address inevitable overlaps between anomalous and normal regions in the feature space, ADERH integrates two complementary concepts: Pitch, which highlights points near hypersphere boundaries, and NDensity, which down-weights hyperspheres centered on sparse (and often anomalous) regions. By averaging these local, density-adjusted ``isolation'' indicators across many random subsets, ADERH yields robust anomaly scores that clearly separate normal from abnormal samples. Extensive experiments on diverse real-world datasets show that ADERH consistently outperforms state-of-the-art methods while maintaining linear runtime scalability and stable performance across varying hyperparameter settings.

IJCAI Conference 2025 Conference Paper

Scalable Graph Classification via Random Walk Fingerprints (Extended Abstract)

• Peiyan Li
• Honglian Wang
• Christian Böhm

We design a lightweight structural feature extraction technique for graph classification. It leverages node subsets and connection strength reflected by random-walk-based heuristics, presenting a scalable, unsupervised, and easily interpretable alternative. We provide theoretical insights into our technical design and establish a relation between the extracted structural features and the graph spectrum. We show our method achieves high levels of computational efficiency while maintaining robust classification accuracy.

IJCAI Conference 2021 Conference Paper

Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces

• Lukas Miklautz
• Lena G. M. Bauer
• Dominik Mautz
• Sebastian Tschiatschek
• Christian Böhm
• Claudia Plant

Deep clustering techniques combine representation learning with clustering objectives to improve their performance. Among existing deep clustering techniques, autoencoder-based methods are the most prevalent ones. While they achieve promising clustering results, they suffer from an inherent conflict between preserving details, as expressed by the reconstruction loss, and finding similar groups by ignoring details, as expressed by the clustering loss. This conflict leads to brittle training procedures, dependence on trade-off hyperparameters and less interpretable results. We propose our framework, ACe/DeC, that is compatible with Autoencoder Centroid based Deep Clustering methods and automatically learns a latent representation consisting of two separate spaces. The clustering space captures all cluster-specific information and the shared space explains general variation in the data. This separation resolves the above mentioned conflict and allows our method to learn both detailed reconstructions and cluster specific abstractions. We evaluate our framework with extensive experiments to show several benefits: (1) cluster performance – on various data sets we outperform relevant baselines; (2) no hyperparameter tuning – this improved performance is achieved without introducing new clustering specific hyperparameters; (3) interpretability – isolating the cluster specific information in a separate space is advantageous for data exploration and interpreting the clustering results; and (4) dimensionality of the embedded space – we automatically learn a low dimensional space for clustering. Our ACe/DeC framework isolates cluster information, increases stability and interpretability, while improving cluster performance.

AAAI Conference 2020 Conference Paper

Deep Embedded Non-Redundant Clustering

• Lukas Miklautz
• Dominik Mautz
• Muzaffer Can Altinigneli
• Christian Böhm
• Claudia Plant

Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space directly has been shown to work unsatisfactory. This has increased interest in combining the high representational power of deep learning with clustering, termed deep clustering. Algorithms of this type combine the non-linear embedding of an autoencoder with a clustering objective and optimize both simultaneously. None of these algorithms try to find multiple non-redundant clusterings. In this paper, we propose the novel Embedded Non-Redundant Clustering algorithm (ENRC). It is the first algorithm that combines neuralnetwork-based representation learning with non-redundant clustering. ENRC can find multiple highly non-redundant clusterings of different dimensionalities within a data set. This is achieved by (softly) assigning each dimension of the embedded space to the different clusterings. For instance, in image data sets it can group the objects by color, material and shape, without the need for explicit feature engineering. We show the viability of ENRC in extensive experiments and empirically demonstrate the advantage of combining non-linear representation learning with non-redundant clustering.

IJCAI Conference 2020 Conference Paper

Online Semi-supervised Multi-label Classification with Label Compression and Local Smooth Regression

• Peiyan Li
• Honglian Wang
• Christian Böhm
• Junming Shao

Online semi-supervised multi-label classification serves a practical yet challenging task since only a small number of labeled instances are available in real streaming environments. However, the mainstream of existing online classification techniques are focused on the single-label case, while only a few multi-label stream classification algorithms exist, and they are mainly trained on labeled instances. In this paper, we present a novel Online Semi-supervised Multi-Label learning algorithm (OnSeML) based on label compression and local smooth regression, which allows real-time multi-label predictions in a semi-supervised setting and is robust to evolving label distributions. Specifically, to capture the high-order label relationship and to build a compact target space for regression, OnSeML compresses the label set into a low-dimensional space by a fixed orthogonal label encoder. Then a locally defined regression function for each incoming instance is obtained with a closed-form solution. Targeting the evolving label distribution problem, we propose an adaptive decoding scheme to adequately integrate newly arriving labeled data. Extensive experiments provide empirical evidence for the effectiveness of our approach.

AAAI Conference 2019 Conference Paper

Network Structure and Transfer Behaviors Embedding via Deep Prediction Model

• Xin Sun
• Zenghui Song
• Junyu Dong
• Yongbo Yu
• Claudia Plant
• Christian Böhm

Network-structured data is becoming increasingly popular in many applications. However, these data present great challenges to feature engineering due to its high non-linearity and sparsity. The issue on how to transfer the link-connected nodes of the huge network into feature representations is critical. As basic properties of the real-world networks, the local and global structure can be reflected by dynamical transfer behaviors from node to node. In this work, we propose a deep embedding framework to preserve the transfer possibilities among the network nodes. We first suggest a degree-weight biased random walk model to capture the transfer behaviors of the network. Then a deep embedding framework is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into the conventional Long Short-Term Memory Network to utilize its sequence prediction ability. To keep the local network neighborhood, we further perform a Laplacian supervised space optimization on the embedding feature representations. Experimental studies are conducted on various real-world datasets including social networks and citation networks. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization and classification, and achieve promising performance compared with state-of-the-art models.

YNIMG Journal 2010 Journal Article

Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease

• Claudia Plant
• Stefan J. Teipel
• Annahita Oswald
• Christian Böhm
• Thomas Meindl
• Janaina Mourao-Miranda
• Arun W. Bokde
• Harald Hampel