Essays about: "Dimensionality Reduction"

Showing result 6 - 10 of 96 essays containing the words Dimensionality Reduction.

1. 6. Decoding communication of non-human species - Unsupervised machine learning to infer syntactical and temporal patterns in fruit-bats vocalizations.

   University essay from Stockholms universitet/Institutionen för data- och systemvetenskap

   Author : Luigi Assom; [2023]
   Keywords : animal decision making; unsupervised machine learning; UMAP; autoencoders; classifiers; bioacoustics; combinatory syntax; animal communication;

   Abstract : Decoding non-human species communication offers a unique chance to explore alternative intelligence forms using machine learning. This master thesis focuses on discreteness and grammar, two of five linguistic areas machine learning can support, and tackles inferring syntax and temporal structures from bioacoustics data annotated with animal behavior. READ MORE

3. 7. Identifying cell type-specific proliferation signatures in spatial transcriptomics data and inferring interactions driving tumour growth

   University essay from Uppsala universitet/Neuroonkologi och neurodegeneration

   Author : Felix Wærn; [2023]
   Keywords : spatial transcriptomics; breast cancer; dimensionality reduction; bioinformatics; deconvolution;

   Abstract : Cancer is a dangerous disease caused by mutations in the host's genome that makes the cells proliferateuncontrollably and disrupts bodily functions. The immune system tries to prevent this, but tumours have methods ofdisrupting the immune system's ability to combat the cancer. READ MORE

4. 8. Heart- and Sapwood Segmentation on Hyperspectral Images using Deep Learning

   University essay from Linköpings universitet/Institutionen för systemteknik

   Author : Samuel Hallin; Simon Samnegård; [2023]
   Keywords : Computer Vision; Hyperspectral Imaging; Heartwood; Sapwood; Deep Learning; Segmentation; Dimensionality Reduction; PCA; PLS; SVM; U-NET; U-within-U-Net;

   Abstract : For manufacturers in the wood industry, an important way to make the production more effective is to automate the process of detecting defects and different attributes on boards. One important attribute on most boards is heartwood and sapwood. READ MORE

5. 9. Dimensionality Reduction in High-Dimensional Profile Analysis Using Scores

   University essay from Linköpings universitet/Tillämpad matematik; Linköpings universitet/Tekniska fakulteten

   Author : Jonathan Vikbladh; [2022]
   Keywords : High-dimensional data; Hypothesis testing; LR test; Linear scores; Multivariate analysis; Profile analysis; Spherical distributions;

   Abstract : Profile analysis is a multivariate statistical method for comparing the mean vectors for different groups. It consists of three tests, they are the tests for parallelism, level and flatness. The results from each test give information about the behaviour of the groups and the variables in the groups. READ MORE

6. 10. Towards topology-aware Variational Auto-Encoders : from InvMap-VAE to Witness Simplicial VAE

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Aniss Aiman Medbouhi; [2022]
   Keywords : Variational Auto-Encoder; Nonlinear dimensionality reduction; Generative model; Inverse projection; Computational topology; Algorithmic topology; Topological Data Analysis; Data visualisation; Unsupervised representation learning; Topological machine learning; Betti number; Simplicial complex; Witness complex; Simplicial map; Simplicial regularization.; Variations autokodare; Ickelinjär dimensionalitetsreducering; Generativ modell; Invers projektion; Beräkningstopologi; Algoritmisk topologi; Topologisk Data Analys; Datavisualisering; Oövervakat representationsinlärning; Topologisk maskininlärning; Betti-nummer; Simplicielt komplex; Vittneskomplex; Simpliciel avbildning; Simpliciel regularisering.;

   Abstract : Variational Auto-Encoders (VAEs) are one of the most famous deep generative models. After showing that standard VAEs may not preserve the topology, that is the shape of the data, between the input and the latent space, we tried to modify them so that the topology is preserved. READ MORE