ID 原文 译文
26025 且降维分成主元空间和残差空间，微小信息得不到充分表示。 And the original spaceis reduced dimensionally into the principal component space and the residual space, so that the tiny information cannot be fully expressed．
26026 深度学习在模式识别方面有成功的应用，深度学习多层次网络对细节进行线性组合表示，但不具备可解释性，仅有训练结果无理论依据，机理分析困难。 Deep learning has been successfully applied in pattern recognition. However, multilevel networks of deep learning represent linear combinations of details but do not have explanatory. Only with the result of training without theoretical basis, it is difficult to carry out mechanistic analysis．
26027 本文提出一种将主元分析思想与深度学习思想结合的故障诊断方法，在原 PCA 基础上先扩维再降维，使得原始空间中不能表达的信息充分表达，且具备可解释性。 This paper presents a fault diagnosis method which combines PCA thought and deep learning thought. Based on the original PCA, this paper first expands and then reduces the dimension, making the inexplicit information in the original space fully expressed and interpreted．
26028 理论和仿真实验分析表明，本文方法能判断出传统 PCA 无法检测的微小故障，提高了故障检测的检出率，且具备可解释性。 Theoretical and simulation experiments show that this method can judge the minor faults which cannot be detected by traditional PCA, improve the detection rate of fault detection and have interpretability．
26029 融合内容语义信息的推荐模型可以有效缓解音乐推荐系统中的数据稀疏性和冷启动问题。 A model that incorporates semantic information can alleviate the sparse data and cold start problems in a music recommendation system．
26030 然而，这些模型是通过最小化预测评分误差学习用户与音乐的全局关系，忽略了用户和音乐隐式特征的细粒度差异。 Current models learn the global relations between users and music by minimizing prediction score error. However, they ignore the fine-grained differences between the implicit features of users and music.
26031 此外，内容语义特征是以推荐任务无关的无监督学习方式提取的，从而导致不精确推荐。 Current models also extract semantic features by unsupervised learning that is irrelevant to recommending, leading to inaccurate recommendations．
26032 为此，本文提出了融合内容表示的度量排序学习推荐模型，该模型是以个性化排序最优化为目标的概率图模型，利用度量学习从全局和细粒度层面挖掘用户音乐偏好。 We propose a metric-ranking-learning recommendation model that incorporates content representation (CAMＲL). This model is a probabilistic graphical model that optimizes a personalized ranking and explores user music preferences through metric learning at both global and fine-grained levels．
26033 为了解决冷启动推荐问题，本文建立了与推荐任务相关的监督学习策略训练内容语义特征提取模型。 To solve the cold start recommendation problem, a supervised learning strategy in relation to the recommendation task is proposed to train the model of content semantic feature exaction．
26034 在KKBOX 和 MIGU 数据集上的实验结果表明，提出的模型显著提升了冷启动音乐推荐的效果，在不同稀疏度数据集上的鲁棒性得到了显著增强。 The results of trials using the KKBOX and MIGU datasets show that the proposed model significantly improves cold start music recommendations when compared with other algorithms; it is also more robust when using sparse datasets．