Name
Abstract | ||
|---|---|---|
Low-dimensional topic models have been proven very useful for modeling a large corpus of documents that share a relatively small number of topics. Dimensionality reduction tools such as Principal Component Analysis or Latent Semantic Indexing (LSI) have been widely adopted for document modeling, analysis, and retrieval. In this paper, we contend that a more pertinent model for a document corpus as the combination of an (approximately) low-dimensional topic model for the corpus and a sparse model for the keywords of individual documents. For such a joint topic-document model, LSI or PCA is no longer appropriate to analyze the corpus data. We hence introduce a powerful new tool called Principal Component Pursuit that can effectively decompose the low-dimensional and the sparse components of such corpus data. We give empirical results on data synthesized with a Latent Dirichlet Allocation (LDA) mode to validate the new model. We then show that for real document data analysis, the new tool significantly reduces the perplexity and improves retrieval performance compared to classical baselines. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1871437.1871475 | CIKM |
Keywords | Field | DocType |
sparse model,real document data analysis,low-dimensional topic model,new model,principal component pursuit,corpus data,joint topic-document model,document modeling,large corpus,document corpus,decomposing background topic,pertinent model,perplexity,principal component,principal component analysis,latent dirichlet allocation,latent semantic indexing,data analysis | Data mining,Perplexity,Latent semantic indexing,Latent Dirichlet allocation,Dimensionality reduction,Information retrieval,Computer science,Principal component pursuit,Topic model,Document modeling,Principal component analysis | Conference |
Citations | PageRank | References |
36 | 1.44 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kerui Min | 1 | 103 | 5.33 |
| Zhengdong Zhang | 2 | 261 | 12.55 |
| John Wright | 3 | 10974 | 361.48 |
| Yi Ma | 4 | 14931 | 536.21 |