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- W2892941852 abstract "A novel approach is put forth that utilizes data similarity, quantified on a graph, to improve upon the reconstruction performance of principal component analysis. The tasks of data dimensionality reduction and reconstruction are formulated as graph filtering operations, that enable the exploitation of data node connectivity in a graph via the adjacency matrix. The unknown reducing and reconstruction filters are determined by optimizing a meansquare error cost that entails the data, as well as their graph adjacency matrix. Working in the graph spectral domain enables the derivation of simple gradient descent recursions used to update the matrix filter taps. Numerical tests in real image datasets demonstrate the better reconstruction performance of the novel method over standard principal component analysis." @default.
- W2892941852 created "2018-10-05" @default.
- W2892941852 creator A5072740145 @default.
- W2892941852 date "2019-03-01" @default.
- W2892941852 modified "2023-10-18" @default.
- W2892941852 title "Graph Filtering For Data Reduction and Reconstruction" @default.
- W2892941852 doi "https://doi.org/10.1109/dcc.2019.00115" @default.
- W2892941852 hasPublicationYear "2019" @default.
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