Name
Abstract | ||
|---|---|---|
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables. The proposed model presents several important properties. First, it accounts for heterogeneous data while can be inferred in linear time with respect to the number of objects and attributes. Second, its Bayesian nonparametric nature allows us to automatically infer the model complexity from the data, i.e., the number of features necessary to capture the latent structure in the data. Third, the latent features in the model are binary-valued variables, easing the interpretability of the obtained latent features in data exploration tasks. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Machine Learning | Interpretability,Data mining,Structural equation modeling,Computer science,Latent class model,Latent variable,Feature model,Parametric statistics,Probabilistic latent semantic analysis,Artificial intelligence,Machine learning,Bayesian probability |
DocType | Volume | Citations |
Journal | abs/1707.08352 | 2 |
PageRank | References | Authors |
0.41 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Isabel Valera | 1 | 196 | 17.95 |
| Melanie F. Pradier | 2 | 3 | 2.12 |
| Zoubin Ghahramani | 3 | 10455 | 1264.39 |