论文标题
基于正规L21的半非矩阵分解
Regularized L21-Based Semi-NonNegative Matrix Factorization
论文作者
论文摘要
我们提出了一种通用数据压缩算法，正则化L21半非矩阵分解（L21 SNF）。 L21 SNF提供了可靠的，基于零件的压缩，适用于混合符号数据，高保真，单个Data点重建至关重要。我们得出了严格的算法收敛证明。通过实验，我们显示了L21 SNF提出的用例优势，包括在许多通用机器学习过程中广泛遇到的高度确定系统的压缩。
We present a general-purpose data compression algorithm, Regularized L21 Semi-NonNegative Matrix Factorization (L21 SNF). L21 SNF provides robust, parts-based compression applicable to mixed-sign data for which high fidelity, individualdata point reconstruction is paramount. We derive a rigorous proof of convergenceof our algorithm. Through experiments, we show the use-case advantages presentedby L21 SNF, including application to the compression of highly overdeterminedsystems encountered broadly across many general machine learning processes.
