A kernel principle component analysis method based on tensor algebra is proposed for feature extraction.It can reduce the huge computation cost due to increasing dimensions

while considering the information of known classes.First the kernel principle component analysis method is applied to each class of targets to build their corresponding feature spaces.Then

the collection of feature spaces is unified into a higher dimensional space after introducing the operation of the tensor product.Hence

a linear principle component analysis method can be directly applied on this feature space in order to construct the proper feature space to both reflect the characters of each class and lower the cost of computation.The recognition experiments showed that the cost of computation and memory can be decreased heavily compared to the approach that builds the feature space by using the kernel principle component analysis method directly.