Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When label...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...
Heterogeneous data co-clustering has attracted more and more attention in recent years due to its high impact on various applications. While the co-clustering algorithms for two t...
Bin Gao, Tie-Yan Liu, Xin Zheng, QianSheng Cheng, ...
Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data a...
This poster describes an integrated set of stories and story-based activities that we have used in product development in IBM Software Group's Lotus product organizations. We...
Majie Zeller, Sandra L. Kogan, Michael J. Muller, ...
Self-adjusting computation enables writing programs that can automatically and efficiently respond to changes to their data (e.g., inputs). The idea behind the approach is to stor...