arXiv:2010.11365
공식 코드 · Papers with Code
On a Guided Nonnegative Matrix Factorization
원문 초록
Fully unsupervised topic models have found fantastic success in document clustering and classification. However, these models often suffer from the tendency to learn less-than-meaningful or even redundant topics when the data is biased towards a set of features. For this reason, we propose an approach based upon the nonnegative matrix factorization (NMF) model, deemed \textit{Guided NMF}, that incorporates user-designed seed word supervision. Our experimental results demonstrate the promise of this model and illustrate that it is competitive with other methods of this ilk with only very little supervision information.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Tri-Learn Graph Fusion Network for Attributed Graph Clustering
Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
HERCULES: Hierarchical Embedding-based Recursive Clustering Using LLMs for Efficient Summarization
MiCo: Multiple Instance Learning with Context-Aware Clustering for Whole Slide Image Analysis
Self-supervised Learning of Echocardiographic Video Representations via Online Cluster Distillation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.