为什么matlab中相同大小的矩阵运行速度差异巨大
I extracted features from last two conv layers from model VGG-19, separately named X and Y. X and Y share the same size 19*19*512. Then I apply dimension reduction to X and Y with PCA. And I get the size 361*20. 20 is the dimension.
Then I compute X's inner product, Y's inner product and the cross-product of X and Y, separately written XX,XY,YY. By now , XX ,YY or XY all share the size of 400*1. When I put XX or XY into linSVM as input, it shows badly efficiency and the accuracy of trainset is quite low(about 50%). However, YY as the input shows 100% accuracy of trainset and shows surprising speed. So it really confuses me. Is it possible?
PCA里面涉及奇异值分解SVD，这种运算跟矩阵自身的性质有关系的（比如矩阵病态时可能性能会急剧下降），同样大小的矩阵，如果矩阵自身性质不好，也可能会耗用更多的计算时间。