Name
Abstract | ||
|---|---|---|
Latent space models (LSMs) provide a principled and effective way to extract hidden patterns from observed data. To cope with two challenges in LSMs: (1) how to capture infrequent patterns when pattern frequency is imbalanced and (2) how to reduce model size without sacrificing their expressiveness, several studies have been proposed to diversify LSMs, which design regularizers to encourage the components therein to be diverse. In light of the limitations of existing approaches, we design a new diversity-promoting regularizer by considering two factors: uncorrelation and evenness, which encourage the components to be uncorrelated and to play equally important roles in modeling data. Formally, this amounts to encouraging the co-variance matrix of the components to have more uniform eigenvalues. We apply the regularizer to two LSMs and develop an efficient optimization algorithm. Experiments on healthcare, image and text data demonstrate the effectiveness of the regularizer. |
Year | Venue | Field |
|---|---|---|
2017 | ICML | Species evenness,Data modeling,Matrix (mathematics),Computer science,Uncorrelated,Artificial intelligence,Optimization algorithm,Machine learning,Eigenvalues and eigenvectors,Expressivity |
DocType | Citations | PageRank |
Conference | 3 | 0.36 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengtao Xie | 1 | 339 | 22.63 |
| Aarti Singh | 2 | 584 | 53.39 |
| Bo Xing | 3 | 7332 | 471.43 |