| { | |
| "paper_id": "O15-3006", | |
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| "date_generated": "2023-01-19T08:10:05.190194Z" | |
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| "title": "Automating Behavior Coding for Distressed Couples Interactions Based on Stacked Sparse Autoencoder Framework using Speech-acoustic Features", | |
| "authors": [ | |
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| "institution": "National Tsing Hua University", | |
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| { | |
| "first": "Po-Hsuan", | |
| "middle": [], | |
| "last": "Chen", | |
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| "institution": "National Tsing Hua University", | |
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| { | |
| "first": "Chi-Chun", | |
| "middle": [], | |
| "last": "Lee", | |
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| "institution": "National Tsing Hua University", | |
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| "email": "cclee@ee.nthu.edu.tw" | |
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| "abstract": "Traditional way of conducting analyses of human behaviors is through manual observation. For example in couple therapy studies, human raters observe sessions of interaction between distressed couples and manually annotate the behaviors of each spouse using established coding manuals. Clinicians then analyze these annotated behaviors to understand the effectiveness of treatment that each couple receives. However, this manual observation approach is very time consuming, and the subjective nature of the annotation process can result in unreliable annotation. Our work aims at using machine learning approach to automate this process, and by using signal processing technique, we can bring in quantitative evidence of human behavior. Deep learning is the current state-of-art machine learning technique. This paper proposes to use stacked sparse autoencoder (SSAE) to reduce the dimensionality of the acoustic-prosodic features used in order to identify the key higher-level features. Finally, we use logistic regression (LR) to perform classification on recognition of high and low rating of six different codes. The method achieves an overall accuracy of 75% over 6 codes (husband's average accuracy of 74.9%, wife's average accuracy of 75%), compared to the previously-published study of 74.1% (husband's average accuracy of 75%, wife's average accuracy of 73.2%) (Black et al., 2013), a total improvement of 0.9%. Our proposed method achieves a higher classification rate by using much fewer number of features (10 times less than the previous work (Black et al., 2013)).", | |
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| "text": "Traditional way of conducting analyses of human behaviors is through manual observation. For example in couple therapy studies, human raters observe sessions of interaction between distressed couples and manually annotate the behaviors of each spouse using established coding manuals. Clinicians then analyze these annotated behaviors to understand the effectiveness of treatment that each couple receives. However, this manual observation approach is very time consuming, and the subjective nature of the annotation process can result in unreliable annotation. Our work aims at using machine learning approach to automate this process, and by using signal processing technique, we can bring in quantitative evidence of human behavior. Deep learning is the current state-of-art machine learning technique. This paper proposes to use stacked sparse autoencoder (SSAE) to reduce the dimensionality of the acoustic-prosodic features used in order to identify the key higher-level features. Finally, we use logistic regression (LR) to perform classification on recognition of high and low rating of six different codes. The method achieves an overall accuracy of 75% over 6 codes (husband's average accuracy of 74.9%, wife's average accuracy of 75%), compared to the previously-published study of 74.1% (husband's average accuracy of 75%, wife's average accuracy of 73.2%) (Black et al., 2013), a total improvement of 0.9%. Our proposed method achieves a higher classification rate by using much fewer number of features (10 times less than the previous work (Black et al., 2013)).", | |
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| "text": "\u5716 1. \u81ea\u7de8\u78bc\u5668 112 \u9673\u67cf\u8ed2\u8207\u674e\u7948\u5747 \u5f9e\u5716 1\uff0c\u8f38\u5165\u503c \uff0c 1,2, \u2026 , \uff0c \u2208 \uff0c\u96b1\u85cf\u5c64(hidden layer)\u4e2d\u7684 \uff0c 1,2, \u2026 , \uff0c \u2208 \uff0c\u6b0a\u91cd\u77e9\u9663(weight matrix) \u2208 \uff0c\u504f\u79fb\u5411\u91cf(bias vector) \u2208 \u3002\u7531\u9019\u4e9b\u56e0 \u5b50(factor)\u69cb\u6210\u6fc0\u6d3b\u51fd\u6578(activation function)\uff0c\u5982\u5f0f(1)\u3002 (1) \u5176\u4e2d 1/ 1 \u70ba sigmoid function \u3002\u8f38\u51fa\u503c \uff0c 1,2, \u2026 , , \u2208 \uff0c \u6b0a\u91cd\u77e9\u9663 \u2208 \uff0c\u504f\u79fb\u5411\u91cf \u2208 \uff0c\u81ea\u7de8\u78bc\u5668\u8f38\u51fa\u70ba\u5f0f(2):", | |
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| "content": "<table><tr><td/><td colspan=\"5\">\u900f\u904e\u8a9e\u97f3\u7279\u5fb5\u5efa\u69cb\u57fa\u65bc\u5806\u758a\u7a00\u758f\u81ea\u7de8\u78bc\u5668\u6f14\u7b97\u6cd5\u4e4b \u900f\u904e\u8a9e\u97f3\u7279\u5fb5\u5efa\u69cb\u57fa\u65bc\u5806\u758a\u7a00\u758f\u81ea\u7de8\u78bc\u5668\u6f14\u7b97\u6cd5\u4e4b \u900f\u904e\u8a9e\u97f3\u7279\u5fb5\u5efa\u69cb\u57fa\u65bc\u5806\u758a\u7a00\u758f\u81ea\u7de8\u78bc\u5668\u6f14\u7b97\u6cd5\u4e4b</td><td>113 \u9673\u67cf\u8ed2\u8207\u674e\u7948\u5747 115 \u9673\u67cf\u8ed2\u8207\u674e\u7948\u5747 117</td></tr><tr><td/><td colspan=\"5\">\u5a5a\u59fb\u6cbb\u7642\u4e2d\u592b\u59bb\u4e92\u52d5\u884c\u70ba\u91cf\u8868\u81ea\u52d5\u5316\u8a55\u5206\u7cfb\u7d71 \u5a5a\u59fb\u6cbb\u7642\u4e2d\u592b\u59bb\u4e92\u52d5\u884c\u70ba\u91cf\u8868\u81ea\u52d5\u5316\u8a55\u5206\u7cfb\u7d71 \u5a5a\u59fb\u6cbb\u7642\u4e2d\u592b\u59bb\u4e92\u52d5\u884c\u70ba\u91cf\u8868\u81ea\u52d5\u5316\u8a55\u5206\u7cfb\u7d71</td></tr><tr><td colspan=\"6\">\u4e00\u6bb5\u8a9e\u97f3\u7d93\u904e\u9810\u8655\u7406\uff0c\u964d\u4f4e\u96dc\u8a0a\u5f71\u97ff\uff0c\u624d\u4e0d\u6703\u5f71\u97ff\u4e4b\u5f8c\u7684\u7279\u5fb5\u64f7\u53d6\uff0c\u800c\u9019\u90e8\u5206\u9810\u8655\u7406\u5728 \u4e0a\u7bc7\u8ad6\u6587\u5df2\u7d93\u88ab\u8655\u7406\u904e\u4e86 (Black et al., 2013)\u3002\u672c\u7bc7\u8ad6\u6587\u6539\u8b8a\u7279\u5fb5\u64f7\u53d6\u65b9\u6cd5\uff0c\u9019\u90e8\u5206\u4e0b\u4e00 \u8868 3. 28 \u7a2e\u7279\u5fb5\u503c\u548c 7 \u7a2e functionals LLDs Functionals \u8868 4. 1st hidden unit \u5206\u6790\u4e08\u592b\u548c\u592a\u592a\u5c0d\u61c9\u5230 6 \u7a2e\u884c\u70ba\u7684\u6e96\u78ba\u7387\uff0c\u7c97\u9ad4\u5b57\u70ba\u8f03\u9ad8\u7684 \u6700\u5f8c\u4e00\u5c64\u7684\u7bc0\u9ede\u6578\u6211\u5011\u8a2d\u70ba 150\uff0c\u5982\u8868 6\uff0c\u5f97\u5230\u6700\u5f8c\u7684\u6e96\u78ba\u7387\u3002\u4f7f\u7528\u7684\u758a\u4ee3\u6b21\u6578\u70ba \u6e96\u78ba 20 \u6b21\uff0c\u03c1 0.1\uff0c\u03bb 0.0001\uff0c\u03b2 1\u3002</td></tr><tr><td colspan=\"6\">\u7ae0\u6703\u4ecb\u7d39\u3002\u5982\u5716 3\uff0c\u6b63\u898f\u5316\u5f8c\u7684\u7279\u5fb5\u503c\uff0c\u4e00\u7a2e\u884c\u70ba\u5305\u542b 372 \u7b46 10 \u5206\u9418\u6703\u8a71(session)\uff0c\u5206 \u70ba\u6709\u6a19\u7c64\u6578\u64da(labeled data)\u548c\u6c92\u6709\u6a19\u7c64\u6578\u64da(unlabeled data)\uff0c\u6c92\u6709\u6a19\u7c64\u6578\u64da\u5229\u7528\u7a00\u758f\u81ea\u7de8 \u78bc\u5668\u4f86\u8a13\u7df4\u7db2\u7d61\u53c3\u6578\uff0c\u8a13\u7df4\u597d\u5f8c\u518d\u628a 140 \u7b46\u6709\u6a19\u7c64\u6578\u64da\u5206\u70ba\u8a13\u7df4\u8cc7\u6599\u548c\u6e2c\u8a66\u8cc7\u6599\uff0c\u8f38\u5165 \u81ea\u8a13\u7df4\u597d\u7684\u7db2\u7d61\u53c3\u6578\uff0c\u7522\u751f\u65b0\u7684\u4e00\u7d44\u7279\u5fb5\u3002\u65b0\u7684\u4e00\u7d44\u7279\u5fb5\u70ba\u4e0b\u4e00\u5c64\u8f38\u5165\u503c\uff0c\u91cd\u8907\u5229\u7528\u5716 3 \u67b6\u69cb\u53ef\u4ee5\u7522\u751f\u66f4\u591a\u5c64\u3002\u6211\u5011\u5e0c\u671b\u65b0\u7684\u7279\u5fb5\u503c\u5c0d\u65bc\u884c\u70ba\u5206\u6578\u5c07\u6709\u66f4\u597d\u7684\u8868\u793a\uff0c\u4e0b\u9762\u7ae0\u7bc0 1. MFCC[0-14] 2. MFB[0-7] 3. F0normlog 4. VAD(speech/no speech) 1. Mean 1 st hidden unit Rated Spouse Acc (%) Bla (%) Pos (%) Neg (%) Sad (%) Hum (%) Avg \u8868 6. 3rd hidden unit \u5206\u6790\u4e08\u592b\u548c\u592a\u592a\u5c0d\u61c9\u5230 6 \u7a2e code \u7684\u6e96\u78ba\u7387\u548c\u4e4b\u524d\u7814\u7a76\u6e96\u78ba\u7387 (%) \u6bd4\u8f03 2. Median 3. Standard deviation 4. Skewness 100 Husband 67.9 76.4 65.7 78.6 52.9 61.4 67.2 Wife 70 73.6 65 74.3 58.6 59.3 66.8 Husband 72.9 76.4 71.4 82.1 57.1 67.1 71.2 1 st Hidden Layer 2 nd Hidden Layer 3 rd Rated Acc Bla Pos Neg Sad Hum Avg Hidden Layer Spouse (%) (%) (%) (%) (%) (%) (%)</td></tr><tr><td>\u6703\u8b49\u660e\u4e4b\u3002 200 300 200</td><td>5. Intensity Wife 150</td><td colspan=\"3\">71.4 Husband 80 82.9</td><td>5. Kurtosis 77.1 64.9 78.6 73.6 84.3 59.3 73.6 74.9 65.7 57.9 70</td></tr><tr><td colspan=\"3\">4. \u5be6\u9a57\u8a2d\u8a08\u548c\u7d50\u679c 6. Jitter Husband 77.1 Wife</td><td>77.9</td><td>80</td><td>6. Max position 82.9 58.6 83.6 72.9 81.4 65 72.1</td><td>67.1 67.9 75 72.6</td></tr><tr><td colspan=\"6\">7. Jitter of Jitter Wife 75.7 Previous method 300 Husband 78.6 72.9 72.1 84.3 60 7. Min position 82.1 71.4 78.6 58.6 4.1 \u7279\u5fb5\u503c 500 \u5982\u5716 4\uff0c\u5229\u7528\u539f\u672c LLDs\uff0c\u5728\u4e09\u7a2e\u5c0d\u8a71\u5340\u9593\u88e1(speaker domain)\uff0c\u4e08\u592b\u6642\u9593\u5340\u9593(husband\u3001 63.6 71.4 73.2 71.7 8. Shimmer Husband 70 78.6 68.6 82.9 55 62.1 69.5 (Black et al., 2013) Wife 77.9 84.3 74.3 80 66.4 67.1 75</td></tr><tr><td colspan=\"6\">, H)\u3001\u592a\u592a\u6642\u9593\u5340\u9593(wife\u3001W)\u548c\u4e0d\u5206\u4eba\u6642\u9593\u5340\u9593(full\u3001F)\u6240\u8aaa\u7684\u53e5\u5b50\uff0c\u5207\u5272\u6210\u4ee5 20%\u53e5\u5b50 \u503c\uff0c\u6700\u5f8c\u5f97\u5230 \u548c \u3002 4.2 \u8cc7\u6599 Wife 74.3 82.1 69.3 80.7 58.6 62.9 71.3 4.4 \u5be6\u9a57\u7d50\u679c\u6bd4\u8f03 \u5716 2. \u5806\u758a\u7a00\u758f\u81ea\u7de8\u78bc\u5668 3.3 \u5be6\u9a57\u67b6\u69cb \u6211\u5011\u4f7f\u7528 3 \u5c64\u96b1\u85cf\u5c64\u7684 SSAE \u4f5c\u70ba\u975e\u76e3\u7763\u5b78\u7fd2\u7684\u67b6\u69cb\uff0c\u4f86\u5f9e\u4f4e\u5c64\u7d1a\u7279\u5fb5(low level feature) \u8a13\u7df4\u6210\u9ad8\u5c64\u7d1a\u7279\u5fb5(high level feature)\uff0c\u7136\u5f8c\u7528 LR \u4f86\u76e3\u7763\u5b78\u7fd2\u4f5c\u8fa8\u8b58\uff0c\u672c\u5be6\u9a57\u7b2c\u4e00\u5c64\u7a00\u758f \u81ea\u7de8\u78bc\u5668\u67b6\u69cb\u5982\u5716 3\u3002 \u70ba\u4e00\u500b\u6642\u9593\u5340\u9593\uff0c\u5207\u5272\u5b8c\u5f8c\u5408\u6210\u4e00\u500b\u884c\u5411\u91cf\uff0c\u884c\u5411\u91cf\u7684\u7279\u5fb5\u503c\uff0c\u518d\u7d93\u7531\u5982\u8868 3 \u6240\u5217\u7684 7 \u7531\u539f\u672c\u8cc7\u6599\u5eab 569 \u7b46\u5c0d\u8a71\u3001117 \u5c0d\u592b\u59bb\uff0c\u7d93\u7531\u4e0a\u7bc7\u8ad6\u6587\u9810\u8655\u7406\u904e\u5f8c(Black et al., 2013)\uff0c\u7522 Husband 75 77.9 69.3 84.3 58.6 65.7 71.8 \u6211\u5011\u6240\u4f7f\u7528\u4e09\u7a2e\u4e0d\u540c\u5c64\u7684\u7a00\u758f\u81ea\u7de8\u78bc\u5668\u548c\u4e4b\u524d\u7684\u8ad6\u6587\u6574\u9ad4\u5e73\u5747\u6e96\u78ba\u7387\u7d50\u679c\u5982\u8868 7\u3002 1000 \u7a2e functionals \u8655\u7406\u904e\u5f8c\uff0c\u7522\u751f\u6700\u5f8c 2940 \u500b\u7279\u5fb5\u503c\u3002\u5728\u8f38\u5165 SSAE \u4ee5\u524d\uff0c\u6211\u5011\u628a\u9019\u4e9b\u7279\u5fb5 \u751f\u6700\u5f8c\u7684 372 \u7b46\u5c0d\u8a71\u3001104 \u5c0d\u592b\u59bb\u3002\u5728 372 \u7b46\u5c0d\u8a71\u88e1\u9762\u4e08\u592b\u548c\u592a\u592a\u90fd\u6703\u88ab\u8a55\u5206\u5230\uff0c\u5c0d\u61c9 Wife 72.1 79.3 69.3 80 53.6 62.9 69.5 \u8868 7. \u6574\u9ad4\u5e73\u5747\u6b63\u78ba\u7387\u5c0d\u65bc\u56db\u7a2e\u4e0d\u540c\u65b9\u6cd5 \u503c\u6b63\u898f\u5316\u5728 0 \u548c 1 \u7684\u5340\u9593\u3002\u8a73\u7d30\u7684\u7279\u5fb5\u503c\u5167\u5bb9\u53ef\u53c3\u8003 (Black et al., 2013) \u3002 \u5728 6 \u7a2e\u884c\u70ba\u6e96\u5247\uff0c\u6211\u5011\u9078\u64c7\u524d 20%\u7684\u5206\u6578\u548c\u5f8c 20%\u7684\u5206\u6578\u7684\u5c0d\u8a71\u7576\u4f5c\u5be6\u9a57\u7684\u8fa8\u8b58\uff0c\u5171 140 \u7b46\u5c0d\u8a71\uff1a\u5169\u7a2e\u6a19\u7c64\u503c 0 \u548c 1\uff0c1 \u70ba\u5c0d\u61c9\u5230\u9ad8\u5206\uff0c0 \u70ba\u5c0d\u61c9\u5230\u4f4e\u5206\u3002\u800c\u5728\u9019\u4e9b\u53d6\u51fa\u4f86\u88ab\u9810\u6e2c \u7684\u5c0d\u8a71\u88e1\uff0c\u592b\u59bb\u6578\u4ecb\u65bc 68 \u5230 77 \u5c0d\uff0c\u5229\u7528\u9019\u4e9b\u884c\u70ba\u5c0d\u61c9\u5230\u592b\u59bb\u5c0d\u6578\u4f86\u4f5c\u4ea4\u53c9\u9a57\u8b49\uff0c1 \u5c0d Previous Husband 78.6 72.9 72.1 84.3 60 71.4 73.2 Method Avg(%) method(Black et al., 2013) Wife 77.9 84.3 74.3 80 66.4 67.1 75 Previous (Black et al., 2013) 74.1</td></tr><tr><td colspan=\"6\">\u800c\u70ba\u4e86\u8b93\u8f38\u5165\u7279\u5fb5\u503c\u66f4\u6709\u6548\u7684\u6b78\u985e\u7fa4\u96c6\u4e26\u4e14\u4e0d\u540c\u7279\u5fb5\u4e4b\u9593\u7684\u5340\u9694\u660e\u986f\uff0c \u592b\u59bb\u4f5c\u9a57\u8b49\uff0c\u5176\u9918\u5c0d\u6578\u4f5c\u8a13\u7df4\uff0c\u91cd\u8907\u5faa\u74b0 6 \u7a2e\u884c\u70ba\u5c0d\u61c9\u5230\u7684\u592b\u59bb\u5c0d\u6578\u4f86\u4f5c\u9a57\u8b49\u3002 SSAE One Layer 72.2</td><td>, \u52a0</td></tr><tr><td colspan=\"6\">\u5165\u7a00\u758f\u9805(sparsity term)\u5982\u5f0f(4)\uff0c\u53d6\u540d\u70ba\u7a00\u758f\u7de8\u78bc\u5668(sparse autoencoder) (Obst, 2014)\u3002 \u8868 5. 2nd hidden unit \u5206\u6790\u4e08\u592b\u548c\u592a\u592a\u5c0d\u61c9\u5230 6 \u7a2e code \u7684\u6e96\u78ba\u7387\uff0c\u7c97\u9ad4\u5b57\u70ba\u8f03\u9ad8\u7684 Two Layers 72.3</td></tr><tr><td colspan=\"6\">, \u5f8c\u7684\u8868\u73fe\uff0c\u5206\u5225\u662f\u96b1\u85cf\u5c64\u7bc0\u9ede(hidden units)\u3001\u8a08\u7b97\u640d\u5931\u51fd\u6578(cost function)\u7684\u758a\u4ee3\u6b21\u6578\u548c\u4e09 , || \u5176 \u4e2d log 1 log \uff0c \u70ba \u7a00 \u758f \u53c3 \u6578 (sparsity parameter) \uff0c \uff0c \u70ba\u63a7\u5236\u7a00\u758f\u9805(sparsity term)\u7684\u53c3\u6578\uff0cq \u70ba\u96b1\u85cf\u5c64\u7684\u7bc0\u9ede\u6578\u3002 300 100 Husband 75 78.6 68.6 83.6 57.9 67.9 71.9 \u2211 \u5728\u9019\u5be6\u9a57\u88e1\uff0c\u6211\u5011\u7528 SSAE \u4f86\u4f5c\u70ba\u975e\u76e3\u7763\u5b78\u7fd2\uff0cLR \u4f86\u76e3\u7763\u5b78\u7fd2\u9810\u6e2c\uff0c\u7559\u4e00\u5c0d\u592b\u59bb\u6cd5\u5247 (leave-one-couple-out)\u7684\u65b9\u5f0f\u4f86\u4f5c\u4ea4\u53c9\u9a57\u8b49\u3002\u4e00\u958b\u59cb\u5148\u7528\u8caa\u5a6a\u8a13\u7df4\u7b97\u6cd5(greedy layerwise) \u9010\u5c64\u9810\u5b78\u7fd2(pre-training)\uff0c\u8a13\u7df4\u5b8c\u53c3\u6578\u521d\u59cb\u503c\u8f38\u5165\u81f3 SSAE\uff0cSSAE \u6709\u4e94\u500b\u56e0\u5b50\u6703\u5f71\u97ff\u6700 1 st Hidden Layer Layer Spouse (%) (%) (%) (%) (%) (%) (%) Hidden Rated Acc Bla Pos Neg Sad Hum Avg 2 nd (4) 4.3 \u5be6\u9a57\u8a2d\u5b9a \u6e96\u78ba\u7387 Three Layers 75.0</td></tr><tr><td colspan=\"6\">\u500b\u8d85\u53c3\u6578(hyper-parameters)\u70ba\u03bb\u3001\u03c1\u3001\u03b2\uff0c\u03bb\u70ba\u6b0a\u91cd\u8870\u6e1b\u53c3\u6578(weight decay parameter)\uff0c\u03c1\u70ba\u7a00 Wife 71.4 80.7 72.9 77.1 58.6 62.9 70.6 3.2 \u5806\u758a\u7a00\u758f\u81ea\u7de8\u78bc\u5668(Stacked Sparse Autoencoder) \u758f\u53c3\u6578(sparsity parameter)\uff0c\u03b2\u70ba\u63a7\u5236\u7a00\u758f\u9805(sparsity term)\u7684\u53c3\u6578\uff0c\u9019\u4e9b\u53c3\u6578\u5728\u7b2c\u4e09\u7ae0\u6709\u4ecb 200 Husband 77.1 77.1 71.4 83.6 57.9 69.3 72.7 \u7531\u591a\u500b\u7a00\u758f\u81ea\u7de8\u78bc\u5668\u9010\u5c64\u8a13\u7df4\u5f8c\uff0c\u5806\u758a\u7d44\u6210\u7684\u67b6\u69cb\u70ba\u5806\u758a\u7a00\u758f\u81ea\u7de8\u78bc\u5668(Stacked Sparse Autoencoder)\uff0c\u5982\u5716 2\uff0c\u6bcf\u4e00\u5c64\u7684\u7de8\u78bc\u5f8c\u8f38\u51fa\u70ba\u4e0b\u4e00\u5c64\u7684\u8f38\u5165\u3002\u5f9e\u5716 2 \u53ef\u770b\u51fa\uff0c\u8f38\u5165\u5c64(Input \u7d39\u904e\u3002\u6211\u5011\u5148\u7528 1 \u5c64\u96b1\u85cf\u5c64\u4f86\u6e2c\u8a66\u6e96\u78ba\u7387\uff0c\u5982\u8868 4\u3002\u900f\u904e\u6539\u8b8a\u4e0d\u540c\u7684\u96b1\u85cf\u5c64\u7bc0\u9ede\u6578\uff0c\u6839 \u64da\u6e96\u78ba\u7387\u4f86\u6c7a\u5b9a\u6211\u5011\u4e0b\u4e00\u5c64\u6240\u4f7f\u7528\u7684\u96b1\u85cf\u5c64\u7bc0\u9ede\u6578\u3002 Wife 72.1 82.1 72 77.1 62.1 65.7 71.9 5. \u7d50\u8ad6</td></tr><tr><td colspan=\"6\">layer)\u7d93\u7531\u7b2c\u4e00\u500b\u7a00\u758f\u81ea\u7de8\u78bc\u5668\u8a13\u7df4\u5b8c\u4e4b\u5f8c\u5f97\u5230\u7b2c\u4e00\u96b1\u85cf\u5c64(Hidden layer1)\u7684 n \u500b\u7bc0\u9ede\uff0c \u7531\u9019 n \u500b\u7bc0\u9ede\u5728\u7d93\u904e\u7b2c\u4e8c\u500b\u7a00\u758f\u81ea\u7de8\u78bc\u5668\u8a13\u7df4\u5f97\u5230\u7b2c\u4e8c\u96b1\u85cf\u5c64(Hidden layer2)\u7684 p \u500b\u7bc0\u9ede\uff0c \u6bcf\u5c64\u7684\u96b1\u85cf\u5c64\u7bc0\u9ede\u53ef\u8996\u70ba\u7531\u4e0a\u4e00\u5c64\u7522\u751f\u65b0\u7684\u4e00\u7d44\u7279\u5fb5\uff0c\u900f\u904e\u9019\u6a23\u9010\u5c64\u8a13\u7df4\u53ef\u4ee5\u8a13\u7df4\u66f4\u591a \u5c64\u3002 \u6211\u5011\u5be6\u9a57\u63a1\u7528\u5806\u758a\u7a00\u758f\u81ea\u7de8\u78bc\u5668(Stacked Sparse Autoencoder, SSAE)\uff0c\u5e0c\u671b\u900f\u904e SSAE \u5f97\u5230\u597d\u7684\u7279\u5fb5\u8868\u793a\u65b9\u5f0f\uff0c\u6700\u5f8c\u7d93\u7531\u5206\u985e\u5668\u7522\u751f\u66f4\u597d\u7684\u6e96\u78ba\u7387\u3002 \u5716 4. \u5be6\u9a57\u7279\u5fb5\u63d0\u53d6\u67b6\u69cb 300 Husband 73.6 76.4 72.1 84.3 58.6 67.1 72 \u73fe\u4eca\u5b58\u5728\u8d8a\u4f86\u8d8a\u591a\u8cc7\u6599\u5eab\uff0c\u5982\u4f55\u5feb\u901f\u4e14\u6e96\u78ba\u9810\u6e2c\u8cc7\u6599\uff0c\u662f\u8fd1\u4f86\u7814\u7a76\u7684\u71b1\u9580\u8b70\u984c\u3002\u5728\u9019\u7bc7 \u5982\u8868 4 \u53ef\u5f97\u77e5\uff0c\u96b1\u85cf\u5c64\u6578\u76ee\u70ba 300 \u7684\u6642\u5019\uff0c\u4e08\u592b\u548c\u592a\u592a\u88ab\u8a55\u5206\u7684 6 \u7a2e\u884c\u70ba\u5e73\u5747\u6e96\u78ba Wife 72.9 80.7 71.4 76.4 55 70 71.3 \u8ad6\u6587\u4e2d\uff0c\u6211\u5011\u63d0\u51fa\u5806\u758a\u7a00\u758f\u81ea\u7de8\u78bc\u5668\u6539\u8b8a\u7279\u5fb5\u63d0\u53d6\u7684\u65b9\u6cd5\u548c\u4ee5\u70ba\u4e3b\u9ad4\u67b6\u69cb\uff0c\u4f86\u6bd4\u8f03\u548c\u4e4b \u7387\u70ba\u6700\u9ad8\uff0c\u4f7f\u7528\u7684\u758a\u4ee3\u6b21\u6578\u70ba 15 \u6b21\uff0c\u03c1 0.1\uff0c\u03bb 0.002\uff0c\u03b2 2\u3002 \u63a5\u4e0b\u4f86\u6e2c\u8a66\u4e8c\u5c64\u96b1\u85cf\u5c64\u7684\u6e96\u78ba\u7387\uff0c\u7b2c\u4e00\u5c64\u96b1\u85cf\u6578\u5df2\u7d93\u6c7a\u5b9a\u597d\u4e86\uff0c\u6211\u5011\u6e2c\u8a66\u7684\u7b2c\u4e8c\u5c64 Previous method Husband 78.6 72.9 72.1 84.3 60 71.4 \u524d\u7814\u7a76\u7684\u6e96\u78ba\u7387\uff0c\u76ee\u7684\u5728\u85c9\u7531\u964d\u4f4e\u7279\u5fb5\u6578\u91cf\uff0c\u63d0\u5347\u8a0a\u606f\u7684\u542b\u91cf\u7684\u65b9\u5f0f\uff0c\u627e\u5230\u76f8\u5c0d\u95dc\u9375\u7684 73.2 \u7279\u5fb5\uff0c\u4f86\u9054\u5230\u66f4\u597d\u7684\u6e96\u78ba\u7387\u4e26\u6e1b\u5c11\u8a13\u7df4\u6642\u9593\u3002\u6700\u5f8c\u7d50\u679c\u4e5f\u8b49\u660e\u4e86\u5229\u7528\u975e\u76e3\u7763\u5b78\u7fd2\u4f86\u8a13\u7df4 \u96b1\u85cf\u5c64\u7bc0\u9ede\u6578\uff0c\u5982\u8868 5\u3002\u5f9e\u8868\u4e2d\u5f97\u77e5\uff0c\u7b2c\u4e8c\u5c64\u96b1\u85cf\u5c64\u7684\u7bc0\u9ede\u6578\u70ba 200 \u7684\u6642\u5019\uff0c\u6e96\u78ba\u7387\u70ba (Black et al., 2013) Wife 77.9 84.3 74.3 80 66.4 67.1 75 \u51fa\u65b0\u7684\u4e00\u7d44\u7279\u5fb5\u503c\uff0c\u7d93\u7531\u76e3\u7763\u5b78\u7fd2\u4f5c\u5206\u985e\uff0c\u6e96\u78ba\u7387\u8f03\u4e4b\u524d\u7814\u7a76\u4f86\u7684\u597d\uff0c\u63d0\u51fa\u65b0\u7684\u65b9\u6cd5\u6574 \u6700\u9ad8\uff0c\u4f7f\u7528\u7684\u758a\u4ee3\u6b21\u6578\u70ba 15 \u6b21\uff0c\u03c1 0.1\uff0c\u03bb 0.0001\uff0c\u03b2 1\u3002 \u9ad4\u5e73\u5747\u70ba 75%\u9ad8\u65bc\u820a\u7684\u7814\u7a76 74.1%\uff0c\u63d0\u5347 0.9%\u3002</td></tr></table>", | |
| "text": "\u7531\u8868 7 \u4e2d\u53ef\u5f97\u77e5 3 \u5c64\u7684 SSAE \u8f03\u4e4b\u524d\u7814\u7a76\u63d0\u9ad8 0.9%\u3002\u4e4b\u524d\u7814\u7a76\u4f7f\u7528 40479 \u500b\u7279\u5fb5\u503c\u4f86\u4f5c \u9810\u6e2c\uff0c\u800c\u6211\u5011\u4f7f\u7528 2940 \u500b\u7279\u5fb5\u503c\uff0c\u7406\u8ad6\u4e0a\u770b\u4f86\u8f03\u591a\u7684\u7279\u5fb5\u503c\u76f8\u5c0d\u65bc\u6e96\u78ba\u7387\u6703\u8f03\u9ad8\uff0c\u4f46\u900f \u904e\u6df1\u5ea6\u5b78\u7fd2\u7684\u65b9\u5f0f\uff0c\u964d\u4f4e\u6578\u64da\u7684\u7dad\u5ea6\uff0c\u627e\u51fa\u76f8\u5c0d\u95dc\u9375\u7684\u7279\u5fb5\uff0c\u5c0d\u65bc\u6e96\u78ba\u7387\u7684\u63d0\u5347\u662f\u6709\u5e6b \u52a9\u7684\uff0c\u5f9e\u8868 7 \u4e2d\u770b\u4f86\u96d6\u7136\u8a13\u7df4 1 \u5c64\u548c 2 \u5c64\u6e96\u78ba\u7387\u8868\u73fe\u6c92\u6709\u6bd4\u8f03\u597d\uff0c\u5728\u4f7f\u7528 3 \u5c64\u4e4b\u5f8c\u5c31\u6709 \u597d\u7684\u8868\u73fe\uff0c\u6b64\u8ad6\u9ede\u7531\u6b64\u53ef\u8b49\u3002", | |
| "type_str": "table" | |
| } | |
| } | |
| } | |
| } |