Journal Article10.1016/J.NEUCOM.2017.07.048
Robust locally linear embedding algorithm for machinery fault diagnosis
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TL;DR: A robust LLE (RLLE) is investigated and a novel fault diagnosis method based on RLLE and support vector machine (SVM) are proposed for machinery fault diagnosis.
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About: This article is published in Neurocomputing. The article was published on 17 Jan 2018. The article focuses on the topics: Nonlinear dimensionality reduction & Embedding.
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Manifold Sensing-Based Convolution Sparse Self-Learning for Defective Bearing Morphological Feature Extraction
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A review of artificial intelligence methods for engineering prognostics and health management with implementation guidelines
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TL;DR: A publicly available algorithm that requires only the same order of magnitude of computational effort as ordinary least squares applied to the full set of covariates is described.
Least Angle Regression
TL;DR: Least Angle Regression (LARS) as discussed by the authors is a new model selection algorithm, which is a useful and less greedy version of traditional forward selection methods such as All Subsets, Forward Selection and Backward Elimination.
Sparse signal reconstruction from limited data using FOCUSS: a re-weighted minimum norm algorithm
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TL;DR: A view of the algorithm as a novel optimization method which combines desirable characteristics of both classical optimization and learning-based algorithms is provided and Mathematical results on conditions for uniqueness of sparse solutions are also given.
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