Principal component analysis (PCA) has been extensively applied in data mining, pattern recognition and information retrieval for unsupervised dimensionality reduction. When labels of data are available, e.g., in a classification or regression task, PCA is however not able to use this information. T
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[ACM Press the 12th ACM SIGKDD international conference - Philadelphia, PA, USA (2006.08.20-2006.08.23)] Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '06 - Supervised probabilistic principal component analysis
โ Scribed by Yu, Shipeng; Yu, Kai; Tresp, Volker; Kriegel, Hans-Peter; Wu, Mingrui
- Book ID
- 118148761
- Publisher
- ACM Press
- Year
- 2006
- Weight
- 891 KB
- Volume
- 0
- Category
- Article
- ISBN-13
- 9781595933393
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To learn concepts over massive data streams, it is essential to design inference and learning methods that operate in real time with limited memory. Online learning methods such as perceptron or Winnow are naturally suited to stream processing; however, in practice multiple passes over the same trai