Matrix Factorization Models for Label Ranking and Graded Multi-Label Prediction
碩士 === 國立臺灣大學 === 資訊工程學研究所 === 101 === Multi-label classification has attracted much attention in these days. The extension of the multi-label classification problem are the label ranking or and graded multi-label prediction problems. In this thesis, we focus on a special case of these two extension...
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| Format: | Others |
| Language: | en_US |
| Published: |
2013
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| Online Access: | http://ndltd.ncl.edu.tw/handle/11826091426476901652 |
| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 101 === Multi-label classification has attracted much attention in these days. The extension of the multi-label classification problem are the label ranking or and graded multi-label prediction problems. In this thesis, we focus on a special case of these two extension problem where only partial ranking or incomplete label are observed. We propose a matrix factorization approach to deal these problems. The merit of the matrix factorization model is that it can learn rating or ranking of labels and model the correlations between labels simultaneously. With this model, we can still learn well because our model considering the correlations between labels during training. We also propose a method to combine instance-based model into model-based approach. The experiments show that the matrix factorization model can outperform the baseline model, especially when our target is low rank matrix or training data is insufficient. Combining instance-based method can further boost the performance of our model. We also compare different loss functions combining with matrix factorization, and show that listwise loss can outperform others.
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