A symmetric nonnegative matrix factorization variant that learns weights over nearest-neighbor slices for similarity and dissimilarity, plus a column-wise orthogonality regularizer, improves clustering on eight benchmarks.
Document clustering based on nonnegative sparse matrix factorization,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Learnable Similarity and Dissimilarity Guided Symmetric Non-Negative Matrix Factorization
A symmetric nonnegative matrix factorization variant that learns weights over nearest-neighbor slices for similarity and dissimilarity, plus a column-wise orthogonality regularizer, improves clustering on eight benchmarks.