| 1. | Our proposed methods , matpca & matflda , can deal with not only the vector pattern , but also matrix pattern
我们提出的基于矩阵表示模式的特征提取方法( matpca和matflda ) ，不仅能直接处理向量表示的模式更能处理矩阵表示的模式，因此避免了上述问题。 |
| 2. | In addition , a vector pattern can be recombined into a matrix pattern using some matrixization technique and then be processed by matpca & matflda
另外对于向量表示的模式，我们通过矩阵化重组将其转化成矩阵表示的形式，然后使用matpca和matflda方法进行特征提取。 |
| 3. | The operating object of all these linear classifiers is vector pattern , i . e . , before applying them , any non - vector pattern should be firstly vectorized into a vector pattern
然而现有的线性分类器几乎都是针对向量模式的,即所有的模式都采用向量表示,要应用于矩阵表示的模式,必须首先将矩阵模式转换成向量模式。 |
| 4. | In above mentioned matpca & matflda , a vector pattern is firstly reshaped into a matrix pattern and then processed by pca & flda . it follows a first - matrixization - then - extraction path
前面提到的matpca和matflda是将向量表示的模式转换成矩阵表示的模式后再分别进行pca和flda的方法，它具有先组合后提取的过程。 |
| 5. | It also analyses two patterns of gis case storage , by grid and vector , finally gives management content of gis case , key technology , workflow of two patterns and the contract between grid pattern and vector pattern
论文分析了gis案例存储管理的两种模式? ?栅格模式与矢量模式，并对这两种模式进行了深入地探讨，归纳出了gis案例存储管理的内容、关键技术以及这两种模式的管理流程，并对这两种管理模式的优缺点进行了对比。 |
| 6. | In these two methods a vector pattern is firstly partitioned into a set of sub - patterns , i . e . each sub - pattern in this set is only a part of the original vector pattern . after the partition , traditional pca & flda are used on these sub - pattern sets for sub - feature extraction
它首先将模式数据适当的分成若干个独立的子模式，然后分别对其子模式集使用pca和flda方法进行特征的提取，最后将所有获得的子特征作为模式的最后特征并用于分类。 |
| 7. | Then feature vectors pattern recognition with neural network are studied . based on the above - mentioned feature extraction methods and pattern recognition method , a gear transmission fault diagnosis system is designed and applied in diagnosing the gear transmission fault
在上述特征向量提取研究和特征向量的识别研究的基础上，用小波包作为信号的特征提取方法和神经网络作为分类器，设计一个变速箱齿轮故障诊断系统并应用于变速箱齿轮的故障诊断。 |
| 8. | But , such a vectorization will bring at least three potential problems : 1 ) structural or local contextual infor mation may be broken down ; 2 ) the higher the dimension of input pattern , the more me mory space are needed for the weight vector related to a classifier ; 3 ) when the dimension of a vector pattern is very high and while the sample size is small , it is easy to be overtrained
如此转换至少会带来三个不足: 1 )空间或结构信息可能会遭到破坏; 2 )由于权向量的维数等于输入模式的维数,当输入模式维数很大时,权值的存储空间相应的会很大; 3 )对于大维数的向量模式,当样本数不多的时候,利用线性分类器易导致过拟合。 |
| 9. | This type of strategy has two main shortcomings : 1 ) useful information for classification task contained in the matrix structure may be jeopardized in the vectorizing procedure ; 2 ) after vectorizing procedure computation complexity in classification task may increase substantially due to the vector pattern representation
这种方法存在着两个主要的缺点： 1 )矩阵模式中对分类有用的结构信息很可能会因为向量化的操作而遭到破坏； 2 )向量化的操作极大的增加了特征提取及随后识别的运算复杂度。 |
