Manifold-Aware Deep Clustering: Maximizing Angles between Embedding Vectors Based on Regular Simplex.
Keitaro TanakaRyosuke SawataShusuke TakahashiPublished in: CoRR (2021)
Keyphrases
- vector space
- graph embedding
- clustering algorithm
- k means
- clustering method
- binary vectors
- nonlinear dimensionality reduction
- manifold embedding
- low dimensional
- cluster analysis
- manifold learning
- locality preserving projections
- data clustering
- unsupervised learning
- lower dimensional
- data points
- laplacian eigenmaps
- spectral clustering
- hierarchical clustering
- fuzzy clustering
- locality preserving
- dimensionality reduction
- multidimensional scaling
- document clustering
- self organizing maps
- high dimensional data
- multi dimensional scaling
- euclidean space
- output space
- simplex algorithm
- principal component analysis
- categorical data
- parameter space
- input data