A survey on heterogeneous transfer learning <s> HTLIC <s> A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising. <s> BIB001 </s> A survey on heterogeneous transfer learning <s> HTLIC <s> Relational learning is concerned with predicting unknown values of a relation, given a database of entities and observed relations among entities. An example of relational learning is movie rating prediction, where entities could include users, movies, genres, and actors. Relations encode users' ratings of movies, movies' genres, and actors' roles in movies. A common prediction technique given one pairwise relation, for example a #users x #movies ratings matrix, is low-rank matrix factorization. In domains with multiple relations, represented as multiple matrices, we may improve predictive accuracy by exploiting information from one relation while predicting another. To this end, we propose a collective matrix factorization model: we simultaneously factor several matrices, sharing parameters among factors when an entity participates in multiple relations. Each relation can have a different value type and error distribution; so, we allow nonlinear relationships between the parameters and outputs, using Bregman divergences to measure error. We extend standard alternating projection algorithms to our model, and derive an efficient Newton update for the projection. Furthermore, we propose stochastic optimization methods to deal with large, sparse matrices. Our model generalizes several existing matrix factorization methods, and therefore yields new large-scale optimization algorithms for these problems. Our model can handle any pairwise relational schema and a wide variety of error models. We demonstrate its efficiency, as well as the benefit of sharing parameters among relations. <s> BIB002 </s> A survey on heterogeneous transfer learning <s> HTLIC <s> In this paper, we present a new learning scenario, heterogeneous transfer learning, which improves learning performance when the data can be in different feature spaces and where no correspondence between data instances in these spaces is provided. In the past, we have classified Chinese text documents using English training data under the heterogeneous transfer learning framework. In this paper, we present image clustering as an example to illustrate how unsupervised learning can be improved by transferring knowledge from auxiliary heterogeneous data obtained from the social Web. Image clustering is useful for image sense disambiguation in query-based image search, but its quality is often low due to imagedata sparsity problem. We extend PLSA to help transfer the knowledge from social Web data, which have mixed feature representations. Experiments on image-object clustering and scene clustering tasks show that our approach in heterogeneous transfer learning based on the auxiliary data is indeed effective and promising. <s> BIB003 </s> A survey on heterogeneous transfer learning <s> HTLIC <s> We introduce a text-based image feature and demonstrate that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. We do not inspect or correct the tags and expect that they are noisy. We obtain the text feature of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. Our text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. We test the performance of this feature using PASCAL VOC 2006 and 2007 datasets. Our feature performs well, consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. <s> BIB004 </s> A survey on heterogeneous transfer learning <s> HTLIC <s> Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset. <s> BIB005 </s> A survey on heterogeneous transfer learning <s> HTLIC <s> The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade. <s> BIB006
Zhu et al. BIB005 proposed an HTL method for image classification tasks called HTL for Image Classification (HTLIC). As discussed previously, image feature spaces contain visual attributes and often lack deterministic semantic meaning thus it may be costly to collect sufficient labeled image data to train an effective classifier on such features. For example, SIFT BIB006 descriptors, which are commonly used to represent visual data from images, model pixel data and lack the conceptual knowledge that can be found in other feature spaces, such as from text documents, to classify an instance. The proposed HTLIC method aims to solve such challenge by enhancing a target image classification task with limited labeled data by exploiting semantic knowledge derived from unlabeled text documents and unlabeled annotated images from an auxiliary source. This unlabeled auxiliary data is relatively inexpensive to collect and can enhance target image classification performance with this process. First, a connection is found between the unlabeled text documents and unlabeled annotated images from an auxiliary source of related concepts. This is done using a two-layer bipartite graph where the top layer represents the relationship between the images and the tags while the bottom layer represents the relationship between the tags and the documents. Thus, from this we can form connections between the images and the text using the annotating tags to bridge the heterogeneous feature spaces gap. Using this connection, we can discover a common semantic space between the text and images by extracting latent semantic features from low-level image features using Latent Semantic Analysis (LSA) BIB001 and learning high-level features through collective matrix factorization (CMF) BIB002 . Once such semantic view is constructed, the images in the target domain can be transformed to match this new enhanced feature space for image classification as it contains greater knowledge for classification than the original. This in turn is expected to provide greater classification performance. A standard classification model can then be trained from the transformed target images to make predictions on test images. In this case, linear SVM's were applied for the experiment. This method differs from aPLSA BIB003 for input requirements, as HTLIC also accepts unlabeled auxiliary documents rather than just unlabeled annotated images. Additionally, HTLIC requires limited target labels while aPLSA does not. Experiments were performed on image classification tasks to compare the proposed HTLIC to PCA, Tag BIB004 , and an SVM trained only on the labeled target images. The proposed method was shown to be effective by having the highest classification accuracy for most tests.