Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 4) HETEROGENEOUS LEARNING <s> In this paper we introduce a novel approach to manifold alignment, based on Procrustes analysis. Our approach differs from "semi-supervised alignment" in that it results in a mapping that is defined everywhere - when used with a suitable dimensionality reduction method - rather than just on the training data points. We describe and evaluate our approach both theoretically and experimentally, providing results showing useful knowledge transfer from one domain to another. Novel applications of our method including cross-lingual information retrieval and transfer learning in Markov decision processes are presented. <s> BIB001 </s> Spatial Big Data and Moving Objects: A Comprehensive Survey <s> 4) HETEROGENEOUS LEARNING <s> A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. <s> BIB002
Another important issue is the heterogeneous property of data that is collected from a wide variety of sources. Data fusion of such heterogeneous data is an extremely challenging task. Although there are several ways to handle the heterogeneous data but two common methods are based on machine learning. The first method uses common space such as Procrustes analysis and canonical correlation BIB001 ,  . These methods assume the linearity of the datasets transformation. Kernel stick is used as another method for transforming linear data to non-linear BIB002 .