Knowledge Transfer with Low-Quality Data: A Feature Extraction Issue

Provided by: Institute of Electrical & Electronic Engineers
Topic: Data Management
Format: PDF
Effectively utilizing readily available auxiliary data to improve predictive performance on new modeling tasks is a key problem in data mining. In this research, the goal is to transfer knowledge between sources of data, particularly when ground-truth information for the new modeling task is scarce or is expensive to collect where leveraging any auxiliary sources of data becomes a necessity. Toward seamless knowledge transfer among tasks, effective representation of the data is a critical but yet not fully explored research area for the data engineer and data miner. Here, the authors present a technique based on the idea of sparse coding, which essentially attempts to find an embedding for the data by assigning feature values based on subspace cluster membership.

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