Name
Abstract | ||
|---|---|---|
Logistic regression is one of the classical approaches for classification which has been widely used in computer vision, bioinformatics as well as multimedia understanding. However, when it is applied to high-dimensional data with structural information such as facial images or motion data, traditional vector-based logistic regression suffers from two main weaknesses: one is its negligence of structural information, and the other is its trend of overfitting. In this paper, we propose Logistic Tensor Regression (LTR) for classification of high-dimensional data with structural information. The proposed LTR not only reserves the underlying structural information embedded in data by tensorial representations, but also avoids overfitting by the introduction of a sparsity regularizer. Experiments on classification of facial images and motion data show that our proposed Logistic Tensor Regression approach outperforms the state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-36669-7_70 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
traditional vector-based logistic regression,motion data,high-dimensional data,proposed ltr,proposed logistic tensor regression,underlying structural information,structural information,logistic regression,logistic tensor regression,facial image | Pattern recognition,Regression,Tensor,Multinomial logistic regression,Logistic model tree,Artificial intelligence,Overfitting,Logistic regression,Mathematics,Machine learning | Conference |
Volume | Issue | ISSN |
7751 LNCS | null | 16113349 |
Citations | PageRank | References |
2 | 0.40 | 9 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu Tan | 1 | 12 | 5.00 |
| Yin Zhang | 2 | 3492 | 281.04 |
| Siliang Tang | 3 | 179 | 33.98 |
| Jian Shao | 4 | 2 | 0.40 |
| Fei Wu | 5 | 2209 | 153.88 |
| Yue-Ting Zhuang | 6 | 3549 | 216.06 |