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Sparse-Coding-Based Autoencoder and Its Application for Cancer Survivability Prediction
2022
Mathematical Problems in Engineering
Cancer-survivability prediction is one of the popular research topics, that attracted great attention from both the health service providers and academia. However, one remaining question comes from how to make full use of a large number of available factors (or features). This paper, accordingly, presents a novel autoencoder algorithm based on the concept of sparse coding to address this problem. The main contribution is twofold: the utilization of sparsity coding for input feature selection
doi:10.1155/2022/8544122
doaj:ccee73d85404432f875d334ccb347ed1
fatcat:mbbmclid4jdo7axusk6vipl5g4