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Data dimension reduction using krylov subspaces: Making adaptive beamformers robust to model order-determination
Hongya Ge
, Ivars P. Kirsteins
, Louis L. Scharf
Electrical and Computer Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
22
Scopus citations
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Dive into the research topics of 'Data dimension reduction using krylov subspaces: Making adaptive beamformers robust to model order-determination'. Together they form a unique fingerprint.
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Keyphrases
Dimensionality Reduction
100%
Reduced Rank
100%
Adaptive Beamformer
100%
Krylov Subspace
100%
Data Dimension Reduction
100%
Model Order Determination
100%
Computational Complexity
50%
Large Array
50%
Model Selection
50%
Adaptive Beamforming
50%
Beamforming Technique
50%
Reduction Strategies
50%
Krylov
50%
Precise Model
50%
Data Dimensionality Reduction
50%
Rank-adaptive
50%
Incorrect Models
50%
Computer Science
Krylov Subspace
100%
Dimensionality Reduction
100%
Data Dimension
100%
Computational Complexity
50%
Preprocessing
50%
Engineering
Dimensionality
100%
Beamformer
100%
Computational Complexity
50%
Adaptive Beamforming
50%
Mathematics
Dimensionality Reduction
100%
Rank Method
50%