Structured Latent Factor Analysis
نویسندگان
چکیده
Latent factor models (LFMs) are a set of unsupervised methods that model observed highdimensional data examples by linear combination of latent factors. To enable efficient processing of large data collections, LFMs aim to find concise descriptions of the members of a data collection while preserving the essential statistical information which is useful for basic tasks such as classification, indexing or summarization. Due to its simple form and computation convenience, latent factor models have been very popular in modeling and analyzing massive data sets such as text documents and images [Hastie et al., 2001].
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