A survey on heterogeneous transfer learning <s> TLRisk <s> This paper investigates a new machine learning strategy called translated learning. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled text data to help learn a model for classifying image data, when the labeled images are difficult to obtain. An important aspect of translated learning is to build a "bridge" to link one feature space (known as the "source space") to another space (known as the "target space") through a translator in order to migrate the knowledge from source to target. The translated learning solution uses a language model to link the class labels to the features in the source spaces, which in turn is translated to the features in the target spaces. Finally, this chain of linkages is completed by tracing back to the instances in the target spaces. We show that this path of linkage can be modeled using a Markov chain and risk minimization. Through experiments on the text-aided image classification and cross-language classification tasks, we demonstrate that our translated learning framework can greatly outperform many state-of-the-art baseline methods. <s> BIB001 </s> A survey on heterogeneous transfer learning <s> TLRisk <s> In this paper, we study the problem of transfer learning from text to images in the context of network data in which link based bridges are available to transfer the knowledge between the different domains. The problem of classification of image data is often much more challenging than text data because of the following two reasons: (a) Labeled text data is very widely available for classification purposes. On the other hand, this is often not the case for image data, in which a lot of images are available from many sources, but many of them are often not labeled. (b) The image features are not directly related to semantic concepts inherent in class labels. On the other hand, since text data tends to have natural semantic interpretability (because of their human origins), they are often more directly related to class labels. Therefore, the relationships between the images and text features also provide additional hints for the classification process in terms of the image feature transformations which provide the most effective results. The semantic challenges of image features are glaringly evident, when we attempt to recognize complex abstract concepts, and the visual features often fail to discriminate such concepts. However, the copious availability of bridging relationships between text and images in the context of web and social network data can be used in order to design for effective classifiers for image data. One of our goals in this paper is to develop a mathematical model for the functional relationships between text and image features, so as indirectly transfer semantic knowledge through feature transformations. This feature transformation is accomplished by mapping instances from different domains into a common space of unspecific topics. This is used as a bridge to semantically connect the two heterogeneous spaces. This is also helpful for the cases where little image data is available for the classification process. We evaluate our knowledge transfer techniques on an image classification task with labeled text corpora and show the effectiveness with respect to competing algorithms. <s> BIB002 </s> A survey on heterogeneous transfer learning <s> TLRisk <s> We present a framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory. The framework suggests an operational retrieval model that extends recent developments in the language modeling approach to information retrieval. A language model for each document is estimated, as well as a language model for each query, and the retrieval problem is cast in terms of risk minimization. The query language model can be exploited to model user preferences, the context of a query, synonomy and word senses. While recent work has incorporated word translation models for this purpose, we introduce a new method using Markov chains defined on a set of documents to estimate the query models. The Markov chain method has connections to algorithms from link analysis and social networks. The new approach is evaluated on TREC collections and compared to the basic language modeling approach and vector space models together with query expansion using Rocchio. Significant improvements are obtained over standard query expansion methods for strong baseline TF-IDF systems, with the greatest improvements attained for short queries on Web data. <s> BIB003
Dai et al. BIB001 proposed a method for heterogeneous transfer learning called Translated Learning via Risk Minimization (TLRisk). This method proposes using a translator to perform a form of an asymmetric mapping by "translating" the features from a source feature space to that of a target feature space as to learn within a single feature space. It uses the language model of BIB003 and combines the use of such feature translation with nearest neighbor learning. This proposed method is modeled by a Markovian chain. The source feature space can be modeled as an initial Markov chain c → y s → x s where the source data x s is represented by features y s . Also, the target can be similarly modeled as c → y t → x t . Thus, the proposed method can be modeled by a new Markovian chain of c → y s → y t → x t which illustrates the transfer of knowledge from a source feature space to that of the target using y s → y t as a feature-level translation. This translation is done by learning a probabilistic model which uses co-occurrence data as a bridge between the source and target feature spaces. Furthermore, the proposed model performs translated learning through an extension of the risk minimization framework of BIB003 . The objective of this is to minimize an expected risk function with respect to the labeled training data and the feature translator. This risk function is formulated as an expected loss of classifying a test instance as a particular class thus we want to minimize the risk function as to have a higher probability of correct classification. Due to computational costs of these operations, the risk function is approximated and the algorithm is implemented using dynamic programming. This method has similarities to TTI BIB002 as it uses a feature translator to solve the differing feature spaces and also uses co-occurrence data as the link. However, they differ BIB002 showing the process of semantic label propagation from source texts to test image using the learned translator. Output is a final prediction function in properties such that TLRisk uses a Markovian chain model and risk minimization and TTI uses the "topic-space" as a common subspace in the translator. Experiments were performed on text-aided image classification and cross-language classification to test the effectiveness of the proposed TLRisk method. This method was evaluated using three different dissimilarity functions when approximating the loss function. The results show that TLRisk outperformed the baselines no matter which dissimilarity function was used demonstrating it as an effective method.