第3期 黄雨婷，等：三角距离相关性的标签分布学习 ·455· 续表11 算法 Euclidean! Sorensenl Squard KLI Intersectiont Fidelityt 平均值 0.0591±0.00080.0597±0.00080.0127±0.00110.0125±0.0007 0.9403±0.0008 0.9968±0.0008 LDLLC 1.2 (1) (1) (1) (1) (1) (2) 0.4500±0.02310.4354±0.01930.5450±0.03610.8678±0.11980.5646±0.0193 0.8180±0.0131 PT-Bayes 9.0 (9) (9) (9) (9 (9) (9) 0.0625±0.00230.0627±0.00220.0141±0.00100.0141±0.00100.9373±0.0022 0.9964±0.0003 PT-SVM 33 (4) (3) (3) (3) (4) (3) 0.0624±0.00200.0632±0.00180.01410.00100.0141±0.00100.9368±0.00180.9964±0.0003 AA-kNN 32 (3) (4) (3) (3) (3) (3) 0.0793±0.00680.0822±0.00710.0235±0.00470.0246±0.00530.9198±0.00610.9937±0.0028 AA-BP 8.0 (8) (8) (8) (8) (8) (8) 0.0703±0.00360.0692±0.00330.0182±0.00160.018240.00160.9309±0.00330.99540.0042 SA-IIS 6.2 (6) (6) (6) (6) (6) (7) 0.0728±0.00310.0791±0.00290.0188±0.00160.0186±0.00150.9304±0.00340.99610.0048 SA-BFGS 6.8 (① (7) (7) (7) (7) (6) 0.0629±0.00160.0633±0.00170.0143±0.00080.0143±0.00080.9366±0.00170.9963±0.0003 EDL 5.0 ( (5) (5) (5) (5) 表12Spo数据集上的实验结果 Table 12 Experimental results on the Spo dataset 算法 Euclidean! Sorensen↓ Squardx KLI Intersection Fidelity 平均值 0.0817±0.00140.0842±0.00140.0247±0.00160.02430.00160.9158±0.00140.9937±0.0006 T-LDL 2.0 (1) (2) (2) (2) (2) (3) 0.0819±0.0013 0.0844±0.00130.0248±0.00140.0245±0.00130.9156±0.0013 0.9937±0.0005 LDLLC 2.7 (2) (3) (3) (3) (3) (2) 0.4038±0.01620.4030±0.01340.49720.02460.717240.08400.5971±0.0134 0.8342±0.0095 PT-Bayes 9.0 (9) (9) (9) (9) (9) (9) 0.087840.00190.0893±0.00220.0280±0.00150.028440.00150.9107±0.0022 0.9929±0.0004 PT-SVM 6.2 () (6) (6) (6) (6) (6) 0.0879±0.00300.0899±0.00240.0286±0.00200.0286±0.00020.9096±0.00340.9927±0.0005 AA-kNN 6.8 (6) (7) (7) (7 (7 (7) 0.0979±0.00410.1012±0.00380.0344±0.00380.035940.00390.8982±0.00370.9906±0.0010 AA-BP 8.0 (8) (8) (8) (8) (8) (8) 0.0863±0.00410.0861±0.00360.0251±0.00360.0252±0.00220.9139±0.00360.9937±0.0005 SA-IIS 4.2 (5) (4) (4) (4) (4) (4) 0.081940.00450.0833±0.00380.0229±0.00190.0226±0.0021 0.9168±0.00390.9951±0.0007 SA-BFGS 1.3 (3) (1) (1) (1) (1) (1) 0.0843±0.00290.0872±0.00290.0268±0.00150.026940.00160.9128±0.00280.9932±0.0004 EDL 4.8 (4) (5) (S) (5) (5) (S) 表l3Cold数据集上的实验结果 Table 13 Experimental results on the Cold dataset 算法 Euclideanl Sorensen Squard KLI Intersectiont Fidelity 平均值 0.0681±0.00150.0591±0.00140.0122±0.00230.0120±0.00130.9409±0.00140.996940.0013 T-LDI 1.0 (1) (1) () (1) (1) (1) 0.068340.00190.059240.00170.0122±0.00250.0121±0.00170.9408±0.00170.9969±0.0012 LDLLC 2.2 (2) (2) (2) (2) (2) (3) 0.5252±0.02240.447940.01890.5873±0.03520.9089±0.10420.5521±0.01890.7991±0.0134 PT-Bayes 9.0 (9) (9) (9) (9) (9) (9)续表 11 算法 Euclidean↓ Sørensen↓ Squard χ2 ↓ KL↓ Intersection↑ Fidelity↑ 平均值 LDLLC 0.0591±0.0008 (1) 0.0597±0.000 8 (1) 0.012 7±0.001 1 (1) 0.012 5±0.0007 (1) 0.940 3±0.0008 (1) 0.9968±0.0008 (2) 1.2 PT-Bayes 0.4500±0.0231 (9) 0.4354±0.019 3 (9) 0.545 0±0.036 1 (9) 0.867 8±0.1198 (9) 0.564 6±0.0193 (9) 0.8180±0.0131 (9) 9.0 PT-SVM 0.0625±0.0023 (4) 0.0627±0.002 2 (3) 0.014 1±0.001 0 (3) 0.014 1±0.0010 (3) 0.937 3±0.0022 (4) 0.9964±0.0003 (3) 3.3 AA-kNN 0.0624±0.0020 (3) 0.0632±0.001 8 (4) 0.014 1±0.001 0 (3) 0.014 1±0.0010 (3) 0.936 8±0.0018 (3) 0.9964±0.0003 (3) 3.2 AA-BP 0.0793±0.0068 (8) 0.0822±0.007 1 (8) 0.023 5±0.004 7 (8) 0.024 6±0.0053 (8) 0.919 8±0.0061 (8) 0.9937±0.0028 (8) 8.0 SA-IIS 0.0703±0.0036 (6) 0.0692±0.003 3 (6) 0.018 2±0.001 6 (6) 0.018 2±0.0016 (6) 0.930 9±0.0033 (6) 0.9954±0.0042 (7) 6.2 SA-BFGS 0.0728±0.0031 (7) 0.0791±0.002 9 (7) 0.018 8±0.001 6 (7) 0.018 6±0.0015 (7) 0.930 4±0.0034 (7) 0.9961±0.0048 (6) 6.8 EDL 0.0629±0.0016 (5) 0.0633±0.001 7 (5) 0.014 3±0.000 8 (5) 0.014 3±0.0008 (5) 0.936 6±0.0017 (5) 0.9963±0.0003 (5) 5.0 表 12 Spo 数据集上的实验结果 Table 12 Experimental results on the Spo dataset 算法 Euclidean↓ Sørensen↓ Squard χ2 ↓ KL↓ Intersection↑ Fidelity↑ 平均值 T-LDL 0.0817±0.0014 (1) 0.0842±0.001 4 (2) 0.024 7±0.001 6 (2) 0.024 3±0.0016 (2) 0.915 8±0.0014 (2) 0.9937±0.0006 (3) 2.0 LDLLC 0.0819±0.0013 (2) 0.0844±0.001 3 (3) 0.024 8±0.001 4 (3) 0.024 5±0.0013 (3) 0.915 6±0.0013 (3) 0.9937±0.0005 (2) 2.7 PT-Bayes 0.4038±0.0162 (9) 0.4030±0.013 4 (9) 0.497 2±0.024 6 (9) 0.717 2±0.0840 (9) 0.597 1±0.0134 (9) 0.8342±0.0095 (9) 9.0 PT-SVM 0.0878±0.0019 (7) 0.0893±0.002 2 (6) 0.028 0±0.001 5 (6) 0.028 4±0.0015 (6) 0.910 7±0.0022 (6) 0.9929±0.0004 (6) 6.2 AA-kNN 0.0879±0.0030 (6) 0.0899±0.002 4 (7) 0.028 6±0.002 0 (7) 0.028 6±0.0002 (7) 0.909 6±0.0034 (7) 0.9927±0.0005 (7) 6.8 AA-BP 0.0979±0.0041 (8) 0.1012±0.003 8 (8) 0.034 4±0.003 8 (8) 0.035 9±0.0039 (8) 0.898 2±0.0037 (8) 0.9906±0.0010 (8) 8.0 SA-IIS 0.0863±0.0041 (5) 0.0861±0.003 6 (4) 0.025 1±0.003 6 (4) 0.025 2±0.0022 (4) 0.913 9±0.0036 (4) 0.9937±0.0005 (4) 4.2 SA-BFGS 0.0819±0.0045 (3) 0.0833±0.003 8 (1) 0.022 9±0.001 9 (1) 0.022 6±0.0021 (1) 0.916 8±0.0039 (1) 0.9951±0.0007 (1) 1.3 EDL 0.0843±0.0029 (4) 0.0872±0.002 9 (5) 0.026 8±0.001 5 (5) 0.026 9±0.0016 (5) 0.912 8±0.0028 (5) 0.9932±0.0004 (5) 4.8 表 13 Cold 数据集上的实验结果 Table 13 Experimental results on the Cold dataset 算法 Euclidean↓ Sørensen↓ Squard χ2 ↓ KL↓ Intersection↑ Fidelity↑ 平均值 T-LDL 0.0681±0.0015 (1) 0.0591±0.0014 (1) 0.0122±0.0023 (1) 0.0120±0.0013 (1) 0.9409±0.0014 (1) 0.9969±0.0013 (1) 1.0 LDLLC 0.0683±0.0019 (2) 0.0592±0.0017 (2) 0.0122±0.0025 (2) 0.0121±0.0017 (2) 0.9408±0.0017 (2) 0.9969±0.0012 (3) 2.2 PT-Bayes 0.5252±0.0224 (9) 0.4479±0.0189 (9) 0.5873±0.0352 (9) 0.9089±0.1042 (9) 0.5521±0.0189 (9) 0.7991±0.0134 (9) 9.0 第 3 期 黄雨婷，等：三角距离相关性的标签分布学习 ·455·