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27 papers · ranked by Valyu relevance

01
BbioRxiv21 Dec 2021
A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data
Yuansong Zeng, Zhuoyi Wei, Fengqi Zhong, Zixiang Pan + 2 more
Clustering analysis is widely utilized in single-cell RNA-sequencing (scRNA-seq) data to discover cell heterogeneity and cell states. While many clustering methods have been developed for scRNA-seq analysis, most of these methods require to provide the number of clusters. However, it is not easy to know the exact…
86relevance
02
AarXiv16 Jan 2026
Clustering High-dimensional Data: Balancing Abstraction and Representation Tutorial at AAAI 2026
Claudia Plant, Lena G. M. Bauer, Christian Böhm
How to find a natural grouping of a large real data set? Clustering requires a balance between abstraction and representation. To identify clusters, we need to abstract from superfluous details of individual objects. But we also need a rich representation that emphasizes the key features shared by groups of objects…
84relevance
03
AarXiv1 Feb 2024
Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters
Georgios Vardakas, Ioannis Papakostas, Aristidis Likas
Well-Separated Clusters Authors: ['Georgios Vardakas' 'Ioannis Papakostas' 'Aristidis Likas'] Unsupervised learning has gained prominence in the big data era, offering a means to extract valuable insights from unlabeled datasets. Deep clustering has emerged as an important unsupervised category, aiming to exploit the…
83relevance
04
BbioRxiv5 Jun 2022
scDeepC3: scRNA-seq Deep Clustering by A Skip AutoEncoder Network with Clustering Consistency
Gang Wu, Junjun Jiang, Xianming Liu
Single-cell RNA sequencing (scRNA-seq) reveals the heterogeneity and diversity among individual cells and allows researchers conduct cell-wise analysis. Clustering analysis is a fundamental step in analyzing scRNA-seq data which is needed in many downstream tasks. Recently, some deep clustering based methods exhibit…
81relevance
05
AarXiv11 Dec 2024
Deep clustering using adversarial net based clustering loss
Kart–Leong Lim
—Deep clustering is a recent deep learning technique which combines deep learning with traditional unsupervised clustering. At the heart of deep clustering is a loss function which penalizes samples for being an outlier from their ground truth cluster centers in the latent space. The probabilistic variant of deep…
81relevance
06
PPubMed20 Feb 2024
Deep clustering using 3D attention convolutional autoencoder for hyperspectral image analysis
Ziyou Zheng, Shuzhen Zhang, Hailong Song, Qi Yan
Deep clustering has been widely applicated in various fields, including natural image and language processing. However, when it is applied to hyperspectral image (HSI) processing, it encounters challenges due to high dimensionality of HSI and complex spatial-spectral characteristics. This study introduces a kind of…
80relevance
07
BbioRxiv5 Jan 2023
A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis
Charles A. Ellis, Robyn L. Miller, Vince D. Calhoun
Machine learning methods have frequently been applied to electroencephalography (EEG) data. However, while supervised EEG classification is well-developed, relatively few studies have clustered EEG, which is problematic given the potential for clustering EEG to identify novel subtypes or patterns of dynamics that could…
80relevance
08
PPubMed22 Oct 2025
A Study of Deep Clustering in Spike Sorting
Eugen-Richard Ardelean, Raluca Laura Portase
Spike sorting is the process of identifying the source neurons for neuronal activity recorded from extracellular electrodes. Traditional spike sorting pipelines separate the process into distinct feature extraction and clustering steps, which may not optimally capture the complex structure of spike data. This study…
79relevance
09
AarXiv2024
Branched Variational Autoencoder Classifiers
Ahmed Salah, David Yevick
– This paper introduces a modified variational autoencoder (VAEs) that contains an additional neural network branch. The resulting "branched VAE" (BVAE) contributes a classification component based on the class labelsto the total loss and therefore imparts categorical information to the latent representation. As a…
79relevance
10
PPubMed3 Feb 2023
scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering
Yunhe Wang, Zhuohan Yu, Shaochuan Li, Chuang Bian + 4 more
The cell is the basic unit of growth and development of an organism and has unique biological functions. The heterogeneity between cells in a cell population has isogenic properties, which can ascend from stochastic expression of genes, proteins and metabolites (). Conventional bulk RNA sequencing (RNA-seq) averages…
79relevance
11
PPubMed14 Apr 2023
GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning
Jun Seo Ha, Hyundoo Jeong, Achraf El Allali
Recent advances in single-cell sequencing techniques have enabled gene expression profiling of individual cells in tissue samples so that it can accelerate biomedical research to develop novel therapeutic methods and effective drugs for complex disease. The typical first step in the downstream analysis pipeline is…
79relevance
12
PPubMed13 Jun 2024
Research on load clustering algorithm based on variational autoencoder and hierarchical clustering Clustering algorithm based on variational autoencoder and hierarchical clustering
Miaozhuang Cai, Yin Zheng, Zhengyang Peng, Chunyan Huang + 2 more
'Haoxia Jiang' 'Luan Carlos de Sena Monteiro Ozelim'] Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning…
79relevance
13
AarXiv29 Nov 2024
An Approach Towards Learning K-means-friendly Deep Latent Representation
Debapriya Roy
Clustering is a long-standing problem area in data mining. The centroid-based classical approaches to clustering mainly face difficulty in the case of high dimensional inputs such as images. With the advent of deep neural networks, a common approach to this problem is to map the data to some latent space of…
79relevance
14
PPubMed21 Apr 2025
Novel Dual-Constraint-Based Semi-Supervised Deep Clustering Approach
Mona Suliman AlZuhair, Mohamed Maher Ben Ismail, Ouiem Bchir, Adrian Barbu
'Adrian Barbu'] Semi-supervised clustering can be viewed as a clustering paradigm that exploits both labeled and unlabeled data to steer learning accurate data clusters and avoid local minimum solutions. Nonetheless, the attempts to refine existing semi-supervised clustering methods are relatively limited when compared…
78relevance
15
AarXiv12 Dec 2024
Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss
Kart–Leong Lim
Jensen-Shannon Divergence Clustering Loss Authors: ['Kart–Leong Lim'] Abstract—Deep clustering is an emerging topic in deep learning where traditional clustering is performed in deep learning feature space. However, clustering and deep learning are often mutually exclusive. In the autoencoder based deep clustering, the…
77relevance
16
BbioRxiv1 Feb 2025
SCGclust: Single Cell Graph clustering using graph autoencoders integrating SNVs and CNAs
Teja Potu, Yunfei Hu, Rituparna Khan, Srinija Dharani + 4 more
Intra-tumor heterogeneity (ITH) is a compounding factor for cancer prognosis and treatment. Single-cell DNA sequencing (scDNA-seq) provides cellular resolution of the variations in a cell and has been widely used to study cancer progression and responses to drug and treatment. While the low coverage scDNA-seq…
77relevance
17
AarXiv26 Jul 2024
Deep Embedding Clustering Driven by Sample Stability
Zhanwen Cheng, Feijiang Li, Jieting Wang, Yuhua Qian
Deep clustering methods improve the performance of clustering tasks by jointly optimizing deep representation learning and clustering. While numerous deep clustering algorithms have been proposed, most of them rely on artificially constructed pseudo targets for performing clustering. This construction process requires…
76relevance
18
AarXiv28 Nov 2023
Bridging Distribution Learning and Image Clustering in High-Dimensional Space
Guanfang Dong, Chenqiu Zhao, Anup Basu
—Distribution learning focuses on learning the probability density function from a set of data samples. In contrast, clustering aims to group similar objects together in an unsupervised manner. Usually, these two tasks are considered unrelated. However, the relationship between the two may be indirectly correlated…
75relevance
19
BbioRxiv8 Jun 2022
Linearization Autoencoder: an autoencoder-based regression model with latent space linearization
Sangyeon Lee, Hanjin Kim, Doheon Lee
Regression analysis is one of the most widely applied methods in many fields including bio-medical study. Dimensionality reduction is also widely used for data preprocessing and feature selection analysis, to extract high-impact features from the predictions. As the complexity of both data and prediction models…
75relevance
20
PPubMed16 Feb 2023
A study on the clusterability of latent representations in image pipelines
Adrian Wheeldon, Alexander Serb
Latent representations are a necessary component of cognitive artificial intelligence (AI) systems. Here, we investigate the performance of various sequential clustering algorithms on latent representations generated by autoencoder and convolutional neural network (CNN) models. We also introduce a new algorithm, called…
73relevance
21
BbioRxiv12 Oct 2023
scDILT: a model-based and constrained deep learning framework for single-cell Data Integration, Label Transferring, and clustering
Xiang Lin, Jianlan Ren, Le Gao, Zhi Wei + 1 more
The scRNA-seq technology enables high-resolution profiling and analysis of individual cells. The increasing availability of datasets and advancements in technology have prompted researchers to integrate existing annotated datasets with newly sequenced datasets for a more comprehensive analysis. It is important to…
72relevance
22
CchemRxiv17 Sep 2024
The generalisation challenge: assessment of the efficacy of acoustic signals for state estimation of lithium-ion batteries via machine learning
Authors not listed
Acoustic measurements of batteries are known to be correlated to their state-of-charge, creating opportunities for state estimation that do not rely on electrical signals. State estimators are typically parametric models fitted from data, often from the broad toolbox of machine learning. Such models can be easily…
65relevance
23
CchemRxiv1 Sep 2022
Latent Spaces for Antimicrobial Peptide Design
Samuel Renaud, Rachael Mansbach
Current antibacterial treatments cannot overcome the rapidly growing resistance of bacteria to antibiotic drugs, and novel treatment methods are required. One option is the development of new antimicrobial peptides (AMPs), to which bacterial resistance build-up is comparatively slow. Deep generative models have…
63relevance
24
CchemRxiv18 Apr 2023
Construction of order-independent molecular fragments space with vector quantised graph autoencoder
Tagir Akhmetshin, Arkadii Lin, Timur Madzhidov, Alexandre Varnek
Autoencoders represent a promising technique for the inverse quantitative structure-activity relationship (QSAR) task. However, undesirable bias, such as atom ordering, affects the neighbourhood behaviour of autoencoders’ latent space and, consequently, usage of the latent vectors as variables in machine-learning…
60relevance
25
CchemRxiv21 Oct 2024
ULaMDyn: Enhancing Excited-State Dynamics Analysis Through Streamlined Unsupervised Learning
Authors not listed
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations.…
58relevance
26
CchemRxiv11 Oct 2023
Design of Enzymes for Biocatalysis, Bioremediation, and Biosensing using Variational Autoencoder-Generated Latent Spaces
Pavel Kohout, Michal Vasina, Marika Majerova, Veronika Novakova + 5 more
Enzymes play a crucial role in sustainable industrial applications, with their optimization posing a formidable challenge due to the intricate interplay among residues. Computational methodologies predominantly rely on evolutionary insights, leveraging homologous sequences to pinpoint conserved and functionally…
56relevance
27
CchemRxiv15 Apr 2025
Comparative Analysis of Molecular Embeddings for Efficient Compound Similarity Search Using Vector Databases
Authors not listed
Compound similarity is fundamental to various cheminformatics analyses, particularly in the drug discovery industry, where the structure-activity principle is central to medicinal chemistry. Historically, binary fingerprints combined with Tanimoto and “Tanimoto-related metrics” (such as Dice, Sørensen–Dice, and…
55relevance

Results for clustering-based autoencoder

A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data

Clustering High-dimensional Data: Balancing Abstraction and Representation Tutorial at AAAI 2026

Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters

scDeepC3: scRNA-seq Deep Clustering by A Skip AutoEncoder Network with Clustering Consistency

Deep clustering using adversarial net based clustering loss

Deep clustering using 3D attention convolutional autoencoder for hyperspectral image analysis

A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis

A Study of Deep Clustering in Spike Sorting

Branched Variational Autoencoder Classifiers

scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering

GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning

Research on load clustering algorithm based on variational autoencoder and hierarchical clustering Clustering algorithm based on variational autoencoder and hierarchical clustering

An Approach Towards Learning K-means-friendly Deep Latent Representation

Novel Dual-Constraint-Based Semi-Supervised Deep Clustering Approach

Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss

SCGclust: Single Cell Graph clustering using graph autoencoders integrating SNVs and CNAs

Deep Embedding Clustering Driven by Sample Stability

Bridging Distribution Learning and Image Clustering in High-Dimensional Space

Linearization Autoencoder: an autoencoder-based regression model with latent space linearization

A study on the clusterability of latent representations in image pipelines

scDILT: a model-based and constrained deep learning framework for single-cell Data Integration, Label Transferring, and clustering

The generalisation challenge: assessment of the efficacy of acoustic signals for state estimation of lithium-ion batteries via machine learning

Latent Spaces for Antimicrobial Peptide Design

Construction of order-independent molecular fragments space with vector quantised graph autoencoder

ULaMDyn: Enhancing Excited-State Dynamics Analysis Through Streamlined Unsupervised Learning

Design of Enzymes for Biocatalysis, Bioremediation, and Biosensing using Variational Autoencoder-Generated Latent Spaces

Comparative Analysis of Molecular Embeddings for Efficient Compound Similarity Search Using Vector Databases

A Parameter-free Deep Embedded Clustering Method for Single-cell RNA-seq Data

Clustering High-dimensional Data: Balancing Abstraction and Representation Tutorial at AAAI 2026

Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters

scDeepC3: scRNA-seq Deep Clustering by A Skip AutoEncoder Network with Clustering Consistency

Deep clustering using adversarial net based clustering loss

Deep clustering using 3D attention convolutional autoencoder for hyperspectral image analysis

A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis

A Study of Deep Clustering in Spike Sorting

Branched Variational Autoencoder Classifiers

scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering

GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning

Research on load clustering algorithm based on variational autoencoder and hierarchical clustering Clustering algorithm based on variational autoencoder and hierarchical clustering

An Approach Towards Learning K-means-friendly Deep Latent Representation

Novel Dual-Constraint-Based Semi-Supervised Deep Clustering Approach

Deep Clustering using Dirichlet Process Gaussian Mixture and Alpha Jensen-Shannon Divergence Clustering Loss

SCGclust: Single Cell Graph clustering using graph autoencoders integrating SNVs and CNAs

Deep Embedding Clustering Driven by Sample Stability

Bridging Distribution Learning and Image Clustering in High-Dimensional Space

Linearization Autoencoder: an autoencoder-based regression model with latent space linearization

A study on the clusterability of latent representations in image pipelines

scDILT: a model-based and constrained deep learning framework for single-cell Data Integration, Label Transferring, and clustering

The generalisation challenge: assessment of the efficacy of acoustic signals for state estimation of lithium-ion batteries via machine learning

Latent Spaces for Antimicrobial Peptide Design

Construction of order-independent molecular fragments space with vector quantised graph autoencoder

ULaMDyn: Enhancing Excited-State Dynamics Analysis Through Streamlined Unsupervised Learning

Design of Enzymes for Biocatalysis, Bioremediation, and Biosensing using Variational Autoencoder-Generated Latent Spaces

Comparative Analysis of Molecular Embeddings for Efficient Compound Similarity Search Using Vector Databases