極度多標籤分類問題是從極大的標籤空間中設法預測出多個標籤。標籤的數量和稀疏性的問題使得普通模型難以處理極度多標籤分類問題。在本研究中，我們提出了一個處理極度多標籤分類問題的方法。我們的方法可以有效地處理龐大的數據集，不管數據集有多大，實驗證明我們的方法都能有效地處理並預測出結果。此外，現在大多數機器學習演算法都被批評為“黑盒子”問題：模型無法說明它如何決定預測。在我們的方法中，透過特殊的非負參數的限制，我們的方法能夠提供可解釋的解釋。實驗證明，該方法能兼具不錯的預測精度並提供可理解的解釋。

Extreme multi-label learning is to seek most relevant subset of labels from an extreme large labels space. The problem of scalability and sparsity makes extreme multi-label hard to learn. In this paper, we propose a framework to deal with these problems. Our approach allows to deal with enormous dataset efficiently. Moreover, most algorithms nowadays are criticized for “black box” problem, which model cannot provide how it decides to make predictions. Through special non-negative constraint, our proposed approach is able to provide interpretable explanation. Experiments show that our method achieves both high prediction accuracy and understandable explanation.

論文審定書 i
摘要 ii
Abstract iii
List of Figures v
List of Table vi
1. Introduction 1
2. Background and Related Work 3
3. Proposed approach 9
4. Experimental result 14
4.1 Datasets 14
4.2 Evaluation Metrics 15
4.3 Performance Comparison 16
4.4 Interpretable Explanation 19
5. Conclusion 23
6. Reference 24

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