論文解讀：Multi-view Clustering via Joint Nonnegative Matrix Factorization
摘要 很多真實世界中的數據集由不同表達和視角組成，這些不同的表達和視角的信息往往互爲補充。爲了整合非監督集合中多個視角的信息，多視覺聚類算法同時聚類不同視角以得到一個聚類結果，這個結果揭示了多個視覺共享一個潛在結構。本文我們提出了一個NMF（基於非負矩陣分解）的多視角聚類算法，該算法尋找一個因式分解，使得多個視角給出一致的聚類結果。本文提出算法的關鍵在對有約束的聯合非負矩陣因式分解過程進行公式化，
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- 1. 一起學習寫論文: Multi-view Clustering via Joint Nonnegative Matrix Factorization
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- 7. Nonnegative Matrix Factorizations for Clustering（矩陣分解聚類）
- 8. 文論瞎讀：Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
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相關文章
- 1. 一起學習寫論文: Multi-view Clustering via Joint Nonnegative Matrix Factorization
- 2. 讀書筆記：Overlapping Community Detection at Scale: A Nonnegative Matrix Factorization Approach
- 3. 從Non-negative Matrix Factorization說說Clustering
- 4. 【論文導讀】MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS
- 5. Non-negative Matrix Factorization 非負矩陣分解
- 6. 論文筆記：Embedding Graph Auto-Encoder with Joint Clustering via Adjacency Sharing
- 7. Nonnegative Matrix Factorizations for Clustering（矩陣分解聚類）
- 8. 文論瞎讀：Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
- 9. 矩陣分解筆記（Notes on Matrix Factorization）
- 10. NMF（non-negative matrix factorization）相關論文[1]