Sandor Szedmak

Canonical Correlation Analysis: An Overview with Application to Learning Methods

We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments, we look at two approaches of retrieving images based on only their content from a text query. We compare orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model.

The 2005 PASCAL visual object classes challenge

The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved.

Learning hierarchical multi-category text classification models

We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is a variant of the Maximum Margin Markov Network framework, where the classification hierarchy is represented as a Markov tree equipped with an exponential family defined on the edges. We present an efficient optimization algorithm based on incremental conditional gradient ascent in single-example subspaces spanned by the marginal dual variables. Experiments show that the algorithm can feasibly optimize training sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. The algorithms predictive accuracy is competitive with other recently introduced hierarchical multi-category or multilabel classification learning algorithms.

A correlation approach for automatic image annotation

The automatic annotation of images presents a particularly complex problem for machine learning researchers. In this work we experiment with semantic models and multi-class learning for the automatic annotation of query images. We represent the images using scale invariant transformation descriptors in order to account for similar objects appearing at slightly different scales and transformations. The resulting descriptors are utilised as visual terms for each image. We first aim to annotate query images by retrieving images that are similar to the query image. This approach uses the analogy that similar images would be annotated similarly as well. We then propose an image annotation method that learns a direct mapping from image descriptors to keywords. We compare the semantic based methods of Latent Semantic Indexing and Kernel Canonical Correlation Analysis (KCCA), as well as using a recently proposed vector label based learning method known as Maximum Margin Robot.

Kernel-Mapping Recommender system algorithms

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given item. In this paper, we propose a new algorithm that we call the Kernel-Mapping Recommender (KMR), which uses a novel structure learning technique. This paper makes the following contributions: we show how (1) user-based and item-based versions of the KMR algorithm can be built; (2) user-based and item-based versions can be combined; (3) more information-features, genre, etc.-can be employed using kernels and how this affects the final results; and (4) to make reliable recommendations under sparse, cold-start, and long tail scenarios. By extensive experimental results on five different datasets, we show that the proposed algorithms outperform or give comparable results to other state-of-the-art algorithms.

Kernel Regression Based Machine Translation

We present a novel machine translation framework based on kernel regression techniques. In our model, the translation task is viewed as a string-to-string mapping, for which a regression type learning is employed with both the source and the target sentences embedded into their kernel induced feature spaces. We report the experiments on a French-English translation task showing encouraging results.

Synthesis of maximum margin and multiview learning using unlabeled data

In this paper we show that the semi-supervised learning with two input sources can be transformed into a maximum margin problem to be similar to a binary support vector machine. Our formulation exploits the unlabeled data to reduce the complexity of the class of the learning functions. In order to measure how the complexity is decreased we use the Rademacher complexity theory. The corresponding optimization problem is convex and it is efficiently solvable for large-scale applications as well.

Saturated systems of homogeneous boxes and the logical analysis of numerical data

Following the general principles of the logical analysis of data methodology, originally developed for the case of binary data, we define a similar approach for the analysis of numerical data. The central concepts of this methodology are those of homogeneous boxes and of saturated systems of homogeneous boxes. The box-clustering heuristic described in this paper is efficient and was applied successfully for the analysis of datasets concerning breast tumors, oil exploration and diabetes.

Support vector machine to synthesise kernels

In this paper, we introduce a new method (SVM_2K) which amalgamates the capabilities of the Support Vector Machine (SVM) and Kernel Canonical Correlation Analysis (KCCA) to give a more sophisticated combination rule that the boosting framework allows. We show how this combination can be achieved within a unified optimisation model to create a consistent learning rule which combines the classification abilities of the individual SVMs with the synthesis abilities of KCCA. To solve the unified problem, we present an algorithm based on the Augmented Lagrangian Method. Experiments show that SVM_2K performs well on generic object recognition problems in computer vision.

Handling phrase reorderings for machine translation

We propose a distance phrase reordering model (DPR) for statistical machine translation (SMT), where the aim is to capture phrase reorderings using a structure learning framework. On both the reordering classification and a Chinese-to-English translation task, we show improved performance over a baseline SMT system.