PCA_matlab

sample data and output

学生编号	语文	数学	物理	化学	英语	历史
1	84	65	61	72	79	81
2	64	77	77	76	55	70
3	65	67	63	49	57	67
4	74	80	69	75	63	74
5	84	74	70	80	74	82

sample =

84	65	61	72	79	81
64	77	77	76	55	70
65	67	63	49	57	67
74	80	69	75	63	74
84	74	70	80	74	82

sampleSize =

 5

dimSize =

 6

meanOfEachDim =

74.2000	72.6000	68.0000	70.4000	65.6000	74.8000

centeredData =

9.8000	-7.6000	-7.0000	1.6000	13.4000	6.2000
-10.2000	4.4000	9.0000	5.6000	-10.6000	-4.8000
-9.2000	-5.6000	-5.0000	-21.4000	-8.6000	-7.8000
-0.2000	7.4000	1.0000	4.6000	-2.6000	-0.8000
9.8000	1.4000	2.0000	9.6000	8.4000	7.2000

C3 =

95.2000	-13.9000	-23.7500	62.1500	100.3500	63.0500
-13.9000	41.3000	32.7500	44.9500	-26.9500	-5.1000
-23.7500	32.7500	40.0000	42.5000	-33.0000	-8.5000
62.1500	44.9500	42.5000	151.3000	53.7000	53.8500
100.3500	-26.9500	-33.0000	53.7000	110.8000	65.9000
63.0500	-5.1000	-8.5000	53.8500	65.9000	43.7000

EVectors =

0.5004	-0.3882	-0.3944	-0.3443	0.2028	0.5326
-0.1327	0.3223	-0.0219	-0.8160	-0.4606	-0.0088
0.1816	0.3080	-0.7089	0.3788	-0.4733	-0.0459
0.0683	-0.2149	0.4523	0.2489	-0.6424	0.5196
0.0307	0.7492	0.1304	0.0965	0.3278	0.5513
-0.8327	-0.2075	-0.3461	0.0297	0.0515	0.3745

Evalues =

-0.0000	0	0	0	0	0
0	0.0000	0	0	0	0
0	0	2.6035	0	0	0
0	0	0	9.8930	0	0
0	0	0	0	163.5103	0
0	0	0	0	0	306.2932

sortedD =

306.2932
163.5103
9.8930
2.6035
0.0000
-0.0000

orderD =

6
5
4
3
2
1

sortedEvalues =

306.2932	0	0	0	0	0
0	163.5103	0	0	0	0
0	0	9.8930	0	0	0
0	0	0	2.6035	0	0
0	0	0	0	0.0000	0
0	0	0	0	0	-0.0000

sortedEvectors =

0.5326	0.2028	-0.3443	-0.3944	-0.3882	0.5004
-0.0088	-0.4606	-0.8160	-0.0219	0.3223	-0.1327
-0.0459	-0.4733	0.3788	-0.7089	0.3080	0.1816
0.5196	-0.6424	0.2489	0.4523	-0.2149	0.0683
0.5513	0.3278	0.0965	0.1304	0.7492	0.0307
0.3745	0.0515	0.0297	-0.3461	-0.2075	-0.8327

sumOfEvalues =

482.3000

percentage =

0.6351

percentage =

0.9741

percentage =

0.9946

percentage =

1.0000

percentage =

1.0000

percentage =

 1

k =

 2

filteredEvalues =

306.2932	0
0	163.5103

filteredEvectors =

0.5326	0.2028
-0.0088	-0.4606
-0.0459	-0.4733
0.5196	-0.6424
0.5513	0.3278
0.3745	0.0515

rowFeatureVectors =

0.5326	-0.0088	-0.0459	0.5196	0.5513	0.3745
0.2028	-0.4606	-0.4733	-0.6424	0.3278	0.0515

rowDataAdjust =

9.8000	-10.2000	-9.2000	-0.2000	9.8000
-7.6000	4.4000	-5.6000	7.4000	1.4000
-7.0000	9.0000	-5.0000	1.0000	2.0000
1.6000	5.6000	-21.4000	4.6000	9.6000
13.4000	-10.6000	-8.6000	-2.6000	8.4000
6.2000	-4.8000	-7.8000	-0.8000	7.2000

FinalData =

16.1486	-10.6168	-23.4021	0.4397	17.4306	0.3745
12.4840	-15.6732	13.6071	-7.7705	-2.6474	0.0515

reconstructData =

85.3331	66.7085	61.3497	70.7706	78.5947	81.4892
65.3667	79.9120	75.9056	74.9522	54.6098	70.0181
64.4944	66.5377	62.6350	49.5003	57.1578	66.7372
72.8584	76.1752	71.6575	75.6201	63.2956	74.5648
82.9474	73.6666	68.4523	81.1568	74.3422	81.1907

sample =

84	65	61	72	79	81
64	77	77	76	55	70
65	67	63	49	57	67
74	80	69	75	63	74
84	74	70	80	74	82

diff =

-1.3331	-1.7085	-0.3497	1.2294	0.4053	-0.4892
-1.3667	-2.9120	1.0944	1.0478	0.3902	-0.0181
0.5056	0.4623	0.3650	-0.5003	-0.1578	0.2628
1.1416	3.8248	-2.6575	-0.6201	-0.2956	-0.5648
1.0526	0.3334	1.5477	-1.1568	-0.3422	0.8093