该方法提取模式的整体特征，可有效避免选取特征量时的困难。
The proposed method extracts global feature of patterns, thus the difficulty during choosing eigen-quantity can be effectively avoided.
为了避免特征点选取相对集中，给出一种随机分块选取特征点对的方法。
Random block feature point selection is developed to avoid feature point close clustering.
非负矩阵分解过程中，适当地选取特征空间的维数能够获得原始数据的局部特征。
The local feature based representation could be obtained by choosing suitable dimension of the feature subspace in NMF.
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