DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Chun, Hyonho | - |
dc.contributor.advisor | 전현호 | - |
dc.contributor.author | Kim, Gwangwoo | - |
dc.date.accessioned | 2023-06-23T19:31:56Z | - |
dc.date.available | 2023-06-23T19:31:56Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1032790&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308924 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 수리과학과, 2023.2,[iv, 32 p. :] | - |
dc.description.abstract | Deep generative models naturally become nonlinear dimension reduction tools to visualize large-scale datasets for revealing latent grouping patterns or identifying outliers. Variational autoencoder (VAE) is a deep generative method equipped with encoder/decoder structures. However, the VAE tends not to show the grouping pattern clearly without additional annotation information. On the other hand, similarity-based dimension reduction methods such as t-SNE or UMAP present a clear grouping pattern even though these methods do not have encoder/decoder structures. To bridge this gap, we propose a new approach that adopts similarity information in the VAE framework. Our proposed method finds lower dimensional representations with clear grouping structures while keeping the encoder/decoder structures in the model. For biological applications, it is crucial to adjust for covariate information such as batch or doner information to find biologically meaningful groups. We then extend our approach to a conditional VAE (CVAE) to incorporate the covariate information in the dimension reduction step. Our method shows great performance on both synthetic and real-world datasets. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Deep Learning-based Nonlinear Dimension Reductio▼aVariational Autoencode▼aUniform Manifold Approximation and Projection▼aSingle-cell RNA sequencing data▼aCovariate Effect | - |
dc.subject | 심층 학습 기반 비선형 차원 축소▼a변분 오토 인코더▼a균등 다양체 근사 및 투영▼a단일 세포 리보핵산 시퀀싱 데이터▼a공변량 효과 | - |
dc.title | Similarity-assisted variational autoencoder for nonlinear dimension reduction with application to single-cell RNA sequencing data | - |
dc.title.alternative | 비선형 차원축소를 위한 유사로를 이용한 변분 오토 인코더와 단일 세포 리보핵산 시퀀싱 데이터의 적용 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :수리과학과, | - |
dc.contributor.alternativeauthor | 김광우 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.