On transductive regression 2006 2 citations ? 0 self

Abstract In this paper, we study semisupervised linear dimensionality reduction. Beyond conventional supervised methods which merely consider labeled instances, the semisupervised scheme allows to leverage abundant and ample unlabeled instances into learning so as to achieve better generalization performance. Under semisupervised settings, our objective is to learn a smooth as well as discriminative subspace and linear dimensionality reduction is thus achieved by mapping all samples into the subspace. Specifically, we present the transductive ...

Abstract In this paper, we address the issue of transducing the object cutout model from an example image to novel image instances. We observe that although object and background are very likely to contain similar colors in natural images, it is much less probable that they share similar color configurations. Motivated by this observation, we propose a local color pattern model to characterize the color configuration in a robust way. Additionally, we propose an edge profile model to modulate the contrast of the image, which enhances edges alon...

Abstract We propose a new transductive learning algorithm for learning optimal linear representations that utilizes unlabeled data. We pose the problem of learning linear representations as an optimization one on the underlying nonlinear manifold. An additional term is used to prefer representations with large ldquomarginsrdquo when classifying unlabeled data in the nearest classifier sense, a generalization of transductive support vector machines to learning representations. Experimental results of the proposed algorithm on face recognition d...

Abstract While inductive modeling is used to develop a model (function) from data of the whole problem space and then to recall it on new data, transductive modeling is concerned with the creation of single model for every new input vector based on some closest vectors from the existing problem space. The model approximates the output value only for this input vector. However, deciding on the appropriate distance measure, on the number of nearest neighbors and on a minimum set of important features/variables is a challenge and is usually based...

Abstract One of the difficulties of color tracking is that color changes in different lighting conditions, and static color models would be inadequate to capture the nonstationary color distribution over time. Although some work has been done on adaptive color models, this problem still needs further investigation. Different from many other approaches, we formulate the nonstationary color tracking problem as a transductive learning problem, in which the generalization of a trained color classifier is only defined on the pixels in a specific im...

In many modern large-scale learning applications, the amount of unlabeled data far exceeds that of labeled data. A common instance of this problem is the transductive setting where the unlabeled test points are known to the learning algorithm. This paper presents a study of regression problems in that setting. It presents explicit VC-dimension error bounds for transductive regression that hold for all bounded loss functions and coincide with the tight classification bounds of Vapnik when applied to classification. It also presents a new transdu...

Abstract One of the difficulties of color tracking is that color changes in different lighting conditions, and static color models would be inadequate to capture the nonstationary color distribution over time. Although some work has been done on adaptive color models, this problem still needs further investigation. Different from many other approaches, we formulate the nonstationary color tracking problem as a transductive learning problem, in which the generalization of a trained color classifier is only defined on the pixels in a specific im...

Abstract We propose a new transductive learning algorithm for learning optimal linear representations that utilizes unlabeled data. We pose the problem of learning linear representations as an optimization one on the underlying nonlinear manifold. An additional term is used to prefer representations with large ldquomarginsrdquo when classifying unlabeled data in the nearest classifier sense, a generalization of transductive support vector machines to learning representations. Experimental results of the proposed algorithm on face recognition d...

Abstract. We develop a new error bound for transductive learning algorithms. The slack term in the new bound is a function of a relaxed notion of transductive stability, which measures the sensitivity of the algorithm to most pairwise exchanges of training and test set points. Our bound is based on a novel concentration inequality for symmetric functions of permutations. We also present a simple sampling technique that can estimate, with high probability, the weak stability of transductive learning algorithms with respect to a given dataset. We...