arXiv Machine Learning September 25, 2026 By Eina Mizui, Tomohiro Shiraishi, Shunichi Nishino, Ichiro Takeuchi

Selective Inference for Deep Clustering in Latent Spaces

Read the original on arXiv Machine Learning →

The paper introduces a selective inference framework tailored for deep clustering that uses a fixed pretrained encoder to map high‑dimensional data into a latent space before clustering. It addresses the complex selection bias arising from the nonlinear transformation and offers a computationally tractable method to perform valid statistical tests on cluster differences. Experiments on synthetic data show controlled Type I error and higher power compared to conservative baselines, while genomic case studies demonstrate the ability to uncover significant cluster differences while properly accounting for selection bias.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Statistics ML

Aug 25

Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures

arXiv:2403.14830v2 Announce Type: replace Abstract: Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensio...

By Zeya Wang, Chenglong Ye

rag

arXiv Machine Learning

Aug 26

How to Achieve the Intended Aim of Deep Clustering Now, without Deep Learning

The paper examines whether Deep Embedded Clustering (DEC) truly overcomes the fundamental limitations of k‑means clustering, such as handling clusters of arbitrary shapes, varied sizes, and densities. Through analysis, it finds that DEC does not exploit the underlying data distribution and therefore fails to address these limitations. Instead, a non‑deep learning approach that leverages distributional information of clusters can achieve the intended goals of deep clustering.

By Kai Ming Ting, Wei-Jie Xu, Hang Zhang

arXiv AI

Sep 21

Federated Deep Clustering Networks for High-Dimensional and Heterogeneous Data

The paper introduces FedDCN, a federated deep clustering network that jointly optimizes reconstruction and clustering losses for high‑dimensional, heterogeneous data. It addresses challenges of non‑IID client data by generating synthetic augmentations and applying geometric regularization to align latent spaces. Experiments show the method’s effectiveness under both IID and non‑IID settings, and the authors outline future research directions.

By Morris Stallmann, Charalampos S. Kouzinopoulos, Marcin Pietrasik, Anna Wilbik

diffusion safety

arXiv AI

Jun 12

scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing

arXiv:2606. 13007v1 Announce Type: cross Abstract: Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity.

By Ping Xu, Pengjiang Li, Tian Du, Zaitian Wang, Jiawei Gu, Ziyue Qiao, Pengfei Wang, Yuanchun Zhou

llms rag diffusion multimodal benchmarks safety

arXiv Computer Vision

4d ago

VLM4Cluster: Benchmarking Deep Clustering In the Era of Vision-Language Pre-training

arXiv:2609.36648v1 Announce Type: new Abstract: Vision-language pre-training has reshaped image clustering, giving rise to language-assisted image clustering (LaIC), which leverages textual semantics...

By Yuanwei Hu, Bo Peng, Yuheng Jia, Xinting Hu, Yadan Luo, Wenjie Zhu

llms multimodal benchmarks safety

arXiv Machine Learning

Aug 4

Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data

arXiv:2608. 00346v1 Announce Type: new Abstract: Data imbalance poses a major challenge in supervised classification, where the majority-class bias contributes to false negatives and overestimates classification accuracy.

By Pulock Das, Yina Hou, Md. Kamrozzaman Bhuiyan, Manar D. Samad

rag benchmarks safety