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Deterministic Column-Based Matrix Decomposition
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DOI：10.1109/TKDE.2009.64.png)
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Abstract
En 中文
In this paper, we propose a deterministic column-based matrix decomposition method. Conventional column-based matrix decomposition (CX) computes the columns by randomly sampling columns of the data matrix. Instead, the newly proposed method (termed as CX_D) selects columns in a deterministic manner, which well approximates singular value decomposition. The experimental results well demonstrate the power and the advantages of the proposed method upon three real-world data sets.
Keywords:
Matrix decomposition
incremental learning
feature extraction
dimensionality reduction
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