Constrained Mean Shift Clustering

Schier, Maximilian; Reinders, Christoph; Rosenhahn, Bodo

doi:10.1137/1.9781611977172.27

Cited by 11 publications

(5 citation statements)

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“…We additionally included the fraction of binary-like features. A feature is considered as binary if the mean shift clustering, using a GPU-accelerated implementation by (Schier, Reinders, and Rosenhahn 2022), applied to the feature distribution on the training set returns exactly 2 clusters. As expected, the quantization leads to more binary features.…”

Section: Groundingmentioning

confidence: 99%

Q-SENN: Quantized Self-Explaining Neural Networks

Norrenbrock,

Rudolph,

Rosenhahn

2024

AAAI

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Explanations in Computer Vision are often desired, but most Deep Neural Networks can only provide saliency maps with questionable faithfulness. Self-Explaining Neural Networks (SENN) extract interpretable concepts with fidelity, diversity, and grounding to combine them linearly for decision-making. While they can explain what was recognized, initial realizations lack accuracy and general applicability. We propose the Quantized-Self-Explaining Neural Network “Q-SENN”. Q-SENN satisfies or exceeds the desiderata of SENN while being applicable to more complex datasets and maintaining most or all of the accuracy of an uninterpretable baseline model, outperforming previous work in all considered metrics. Q-SENN describes the relationship between every class and feature as either positive, negative or neutral instead of an arbitrary number of possible relations, enforcing more binary human-friendly features. Since every class is assigned just 5 interpretable features on average, Q-SENN shows convincing local and global interpretability. Additionally, we propose a feature alignment method, capable of aligning learned features with human language-based concepts without additional supervision. Thus, what is learned can be more easily verbalized. The code is published: https://github.com/ThomasNorr/Q-SENN

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Section: Groundingmentioning

confidence: 99%

Q-SENN: Quantized Self-Explaining Neural Networks

Norrenbrock,

Rudolph,

Rosenhahn

2024

AAAI

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“…These methods involve selecting instances that are located in areas of high data density. Density-based methods such as OPTICS [17] and mean shift [18,19] have been used to identify areas of high data density and select representative instances from these areas. The proposed technique in [20] can be considered a form of density-based generative sampling.…”

Section: Density-based Samplingmentioning

confidence: 99%

Preserving Data Distribution in Sampling and Instance Selection with Renyi's Divergence

Sadoghi-Yazdi,

Ashkezari-Toussi,

Ramezanzadeh-Yazdi

2023

Preprint

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This paper presents a novel method for sampling based on a distribution function. By leveraging Renyi's divergence criterion, a recursive formulation is directly derived from the disparity between the original and estimated distributions. The Gradient descent algorithm is employed to achieve this recursive equation, and then new samples are derived. Since this method uses the distribution of the original data, it has the ability to preserve the data distribution in the sampled dataset. In instances where the original distribution is unknown, Kernel Density Estimation is employed for estimation. Experimental results demonstrate that the proposed method effectively preserves the data distribution and prevents any alteration in the data concept while maintaining the predictability of the model over selected instances. More precisely, this method has successfully reduced the dataset size by approximately $70\%$, while the accuracy of the learning algorithm has remained unaffected.

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“…The constrained mean shift (CMS) model [29] is a semisupervised model based on density-based mean shift clustering. It uses kernel distance to reduce the appeal of sampling and create a unique data distribution for each sampling cluster point.…”

Section: Baseline Modelsmentioning

confidence: 99%

Gravity Sub-centroids for Optimal Clustering

Mustafa,

Qingyuan,

Jianbo

et al. 2023

Preprint

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This work highlights issues that were not deeply investigated in previous studies on clustering solutions, which have essential impacts on performance in long-term real-world applications that are challenging to detect instantly. Thus, we addressed these issues by proposing two novel techniques: first, we expand the idea of clustering based on centroids to multiple sub-centroids that assist assignment functions in finding the optimal solution. In contrast to recent studies, we extended the concept of gravitational force toward clustering solutions. Finally, the introduced gap generation concept has been associated with these techniques to support a superior clustering solution. Our model is termed semi-supervised gravity clustering (SSGC). To demonstrate the strength of SSGC, we consider multiple performance measurements besides the traditional ones to validate the clustering models in various scenarios. The experimental results show that SSGC outperforms baseline models and successfully obtains the best performance of 30 different domain datasets. Finally, our methodology code is already released.

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Constrained Mean Shift Clustering

Cited by 11 publications

References 0 publications

Q-SENN: Quantized Self-Explaining Neural Networks

Q-SENN: Quantized Self-Explaining Neural Networks

Preserving Data Distribution in Sampling and Instance Selection with Renyi's Divergence

Gravity Sub-centroids for Optimal Clustering

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