(b)步骤如下：（1）求x与y的平均值，然后对所有样例减去对应的平均值

(b) The steps are as follows: (1) Find the average value of x and y, and then subtract the corresponding average value from all samples, and get: <br>(2) Find the characteristic covariance matrix, the data is two-dimensional, so the matrix is ​​2X2 <br>(3 ) Find the eigenvalues ​​and eigenvectors <br>(4) Sort the eigenvalues ​​from large to small, select the largest X, and then use the corresponding large eigenvectors as column vectors to form the eigenvector matrix <br>(5) Project the sample points to Selected feature vector

(b) The steps are as follows: (1) to find the average of x and y, and then subtract the corresponding average for all samples, to:<br>(2) Find the characteristic covance matrix, data two-dimensional, so the matrix is 2X2<br>(3) Find feature values and feature vectors<br>(4) Sort the feature values from large to small, select the largest X of them, and then form the feature vector matrix as column vectors, respectively<br>(5) Project the sample point onto the selected feature vector

(b) The steps are as follows: (1) find the average value of X and y, and then subtract the corresponding average value from all samples<br>(2) Find the characteristic covariance matrix, the data is two-dimensional, so the matrix is 2x2<br>(3) Finding eigenvalues and eigenvectors<br>(4) The eigenvalues are sorted from large to small, and the largest X of them are selected. Then the corresponding large eigenvectors are used as column vectors to form eigenvector matrix<br>(5) The sample points are projected onto the selected feature vectors<br>