Titlebook: Robust Latent Feature Learning for Incomplete Big Data; Di Wu Book 2023 The Author(s), under exclusive license to Springer Nature Singapor

用肘

Conclusion and Outlook, and incomplete (HDI) data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing HDI data lies in addressing the uncertainty problem caused by their incomplete characteristics and some outliers (e.g., noises).

BRACE

Robust Latent Feature Learning based on Smooth ,-norm,is usually represented by a matrix. For example, it is common to see a user-item rating matrix in RSs [6–9], where each row represents a specific user, each column represents a specific item, and each entry represents the user’s preference for an item.

CANE

Data-characteristic-aware Latent Feature Learning,ems, 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.

vocation

Posterior-neighborhood-regularized Latent Feature Learning,ervices are often performed to retrieve QoS data [10, 11]. However, in real applications, the number of candidate services is usually large. Therefore, checking all candidate Web services is expensive, time-consuming, and therefore impractical [6, 12, 13].

騷動(dòng)

wall-stress

Heretical

Robust Latent Feature Learning based on Smooth ,-norm, social networks, wireless sensor networks, and intelligent transportation. In these applications, the relationship between the two types of entities is usually represented by a matrix. For example, it is common to see a user-item rating matrix in RSs [6–9], where each row represents a specific user

muscle-fibers

Improving Robustness of Latent Feature Learning Using ,-Norm,s) to filter the required information is a very challenging problem [5, 6]. Up to now, various methods have been proposed to implement an RS, among which collaborative filtering (CF) is very popular [7–13].

無(wú)關(guān)緊要

Data-characteristic-aware Latent Feature Learning,odel 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 fea

間諜活動(dòng)

Posterior-neighborhood-regularized Latent Feature Learning,, you can select and recommend Web services that meet the quality of service requirements of potential users. Warm-up tests that directly invoke Web services are often performed to retrieve QoS data [10, 11]. However, in real applications, the number of candidate services is usually large. Therefore