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Data-characteristic-aware Latent Feature Learning

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Robust Latent Feature Learning for Incomplete Big Data

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

Some state-of-the-art missing data predictors are primarily based on a latent feature learning (LFL)-based model [1–14]. They improved the basic LFL model based on the neighborhood information of historical recorded data [15–17]. While they have some limitations as follows:To address the above problems, a data-characteristic-aware latent factor (DCALF) model is proposed in [55]. Its main idea is towfold: (1) it first extracts the dense latent features from the original raw HDI data by an LFL model, and (2) it employs DPClust method [21] to simultaneously identify the neighborhoods and outliers of HDI data on the extracted latent features.

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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, China

    Di Wu

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  1. Di Wu
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Wu, D. (2023). Data-characteristic-aware Latent Feature Learning. In: Robust Latent Feature Learning for Incomplete Big Data. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-19-8140-1_6

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  • DOI: https://doi.org/10.1007/978-981-19-8140-1_6

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  • Online ISBN: 978-981-19-8140-1

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