Survey of spectral clustering based on graph theory
Spectral clustering converts the data clustering problem to the graph cut problem. It is based
on graph theory. Due to the reliable theoretical basis and good clustering performance …
on graph theory. Due to the reliable theoretical basis and good clustering performance …
Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning
Background We designed an algorithm to assess COVID-19 patients severity and dynamic
intubation needs and predict their length of stay using the breathing frequency (BF) and …
intubation needs and predict their length of stay using the breathing frequency (BF) and …
Subspace clustering via joint ℓ1, 2 and ℓ2, 1 norms
Some of the data collected from practical applications are usually heavily corrupted. In
subspace clustering, the common method is to use the specific regularization strategy to …
subspace clustering, the common method is to use the specific regularization strategy to …
A sampling-based approach for efficient clustering in large datasets
We propose a simple and efficient clustering method for high-dimensional data with a large
number of clusters. Our algorithm achieves high-performance by evaluating distances of …
number of clusters. Our algorithm achieves high-performance by evaluating distances of …
Evolutionary variational optimization of generative models
J Drefs, E Guiraud, J Lücke - Journal of machine learning research, 2022 - jmlr.org
We combine two popular optimization approaches to derive learning algorithms for
generative models: variational optimization and evolutionary algorithms. The combination is …
generative models: variational optimization and evolutionary algorithms. The combination is …
Generic unsupervised optimization for a latent variable model with exponential family observables
H Mousavi, J Drefs, F Hirschberger, J Lücke - Journal of machine learning …, 2023 - jmlr.org
Latent variable models (LVMs) represent observed variables by parameterized functions of
latent variables. Prominent examples of LVMs for unsupervised learning are probabilistic …
latent variables. Prominent examples of LVMs for unsupervised learning are probabilistic …
Segmentary group-sparsity self-representation learning and spectral clustering via double L21 norm
With the rapid expansion of data dimensions, subspace representation learning, a method
for mapping high-dimensional data samples to their corresponding underlying low …
for mapping high-dimensional data samples to their corresponding underlying low …
3-D Reconstruction of Ship Target Based on SAR Images Sequence and Scatterer Tracking Technique
R Cao, Y Wang, E Giusti… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Single-channel synthetic aperture radar (SAR) can be applied to reconstruct the three-
dimensional (3-D) structure of maritime targets with the advantages of concise hardware …
dimensional (3-D) structure of maritime targets with the advantages of concise hardware …
Direct evolutionary optimization of variational autoencoders with binary latents
J Drefs, E Guiraud, F Panagiotou, J Lücke - Joint European Conference on …, 2022 - Springer
Many types of data are generated at least partly by discrete causes. Deep generative
models such as variational autoencoders (VAEs) with binary latents consequently became …
models such as variational autoencoders (VAEs) with binary latents consequently became …
Efficient spatio-temporal feature clustering for large event-based datasets
Event-based cameras encode changes in a visual scene with high temporal precision and
low power consumption, generating millions of events per second in the process. Current …
low power consumption, generating millions of events per second in the process. Current …