求翻译：我的问题有什么区别从其他稀疏PCA配方是投影只涉及非负权重的原始坐标——所需的质量在各种领域，包括经济学，生物信息学和计算机视觉。添加非负性有助于稀疏，它执行一个分区的原始坐标轴之间的新。我们描述了一个简单而有效的迭代坐标下降型方案收敛到一个局部最优的优化准则，给出大的现实世界的数据集的好结果。是什么意思？

My question what is the difference from other sparse PCA projection formula involves only non- negative weights of the original coordinates - the desired quality in a variety of fields , including economics , bioinformatics and computer vision .

My question what is the difference from other sparse PCA recipe is a projection involves only non-negative weight of the original coordinates - the required quality in a variety of areas, including economics, bio-informatics and Computer Vision. Add a non-negative, help the sparse, it performs a par

My question has any difference is the projection only involves the quality from other sparse PCA formula which the non-negative weight the primitive coordinates - - need in each kind of domain, including economic, biological information study and computer vision.The increase nonnegativity is helpful

My question what is the difference from other sparse PCA formulation is the original coordinates of the projection involving only non-negative weights – the quality required in a variety of fields, including economics, Bioinformatics and computer vision. Add non-negative help is sparse, it performs

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