Name
Abstract | ||
|---|---|---|
Text categorization is widely characterized as a problem. Robust modeling of the semantic similarity between a query text and training texts is essential to construct an effective and accurate classifier. In this paper, we systematically investigate the Web page/text classification problem via integrating sparse representation with random measurements. In particular, we first adopt a very sparse data-independent random measurement matrix to map the original high dimensional text feature space to a lower dimensional space without loss of key information. We then propose a generic sparse representation method to obtain the sparse solution by decoding the semantic correlations between the query text and entire training samples. Based on the above method, we also design and examine a series of rules by taking advantage of the sparse coefficients to propagate multiple labels for the given query texts. We have conducted extensive experiments using real-world datasets to examine our proposed approach, and the results show the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s11280-017-0460-2 | World Wide Web |
Keywords | Field | DocType |
Multi-label classification,Sparse representation,Random projection | Random projection,Data mining,Computer science,Matrix (mathematics),Multi-label classification,Artificial intelligence,Classifier (linguistics),Semantic similarity,Feature vector,Pattern recognition,Sparse approximation,Decoding methods,Machine learning | Journal |
Volume | Issue | ISSN |
21 | 2 | 1386-145X |
Citations | PageRank | References |
7 | 0.43 | 30 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lina Yao | 1 | 981 | 93.63 |
| Quan Z. Sheng | 2 | 3520 | 301.77 |
| Xianzhi Wang | 3 | 276 | 40.32 |
| Shengrui Wang | 4 | 847 | 65.89 |
| Xue Li | 5 | 2196 | 186.96 |
| Sen Wang | 6 | 477 | 37.24 |