Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference.
David M. MimnoMoontae LeePublished in: EMNLP (2014)
Keyphrases
- low dimensional
- high dimensional
- manifold learning
- high dimensional data
- dimensionality reduction
- vector space
- euclidean space
- dimension reduction
- nonlinear dimensionality reduction
- data points
- principal component analysis
- topic models
- graph embedding
- multidimensional scaling
- lower dimensional
- feature space
- belief networks
- probabilistic inference
- bayesian networks
- inference process
- input space
- topic modeling
- search engine
- linear subspace
- information retrieval
- neural network
- distance measure
- low dimensional manifolds
- low dimensional spaces