Doubly sparse factor models for unifying feature transformation and feature selection
نویسندگان
چکیده
منابع مشابه
Unsupervised feature selection for sparse data
Feature selection is a well-known problem in machine learning and pattern recognition. Many high-dimensional datasets are sparse, that is, many features have zero value. In some cases, we do not known the class label for some (or even all) patterns in the dataset, leading us to semi-supervised or unsupervised learning problems. For instance, in text classification with the bag-of-words (BoW) re...
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ژورنال
عنوان ژورنال: Journal of Physics: Conference Series
سال: 2010
ISSN: 1742-6596
DOI: 10.1088/1742-6596/233/1/012021