Name
Abstract | ||
|---|---|---|
In this paper we explore the topic of un- supervised learning in the presence of non- ignorable missing data with an unknown missing data mechanism. We discuss sev- eral classes of missing data mechanisms for categorical data and develop learning and in- ference methods for two specic models. We present empirical results using synthetic data which show that these algorithms can recover both the unknown selection model parame- ters and the underlying data model param- eters to a high degree of accuracy. We also apply the algorithms to real data from the domain of collaborative ltering, and report initial results. |
Year | Venue | Field |
|---|---|---|
2005 | AISTATS | Semi-supervised learning,Collaborative filtering,Inference,Categorical variable,Computer science,Synthetic data,Unsupervised learning,Artificial intelligence,Missing data,Data model,Machine learning |
DocType | Citations | PageRank |
Conference | 7 | 1.32 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benjamin Marlin | 1 | 950 | 95.15 |
| Sam T. Roweis | 2 | 4556 | 497.42 |
| Richard S. Zemel | 3 | 4958 | 425.68 |