This paper gives an attempt to explore the manifold in the label space for multi-label learning. Traditional label space is logical, where no manifold exists. In order to study the label manifold, the label space should be extended to a Euclidean space. However, the label manifold is not explicitly available from the training examples. Fortunately, according to the smoothness assumption that th...

The aim of this report is to study the two nonlinear manifold learning techniques recently proposed (Isomap [12] and Locally Linear Embedding (LLE) [8]) and suggest directions for further research. First the Isomap and the LLE algorithm are discussed in detail. Some of the areas that need further work are pointed out. A few novel applications which could use these two algorithms have been discu...

The need to reduce the dimensionality of a dataset whilst retaining inherent manifold structure is key in many pattern recognition, machine learning and computer vision tasks. This process is often referred to as manifold learning since the structure is preserved during dimensionality reduction by learning the intrinsic low-dimensional manifold that the data lies on. Since the inception of mani...

Inputs coming from high-dimensional spaces are common in many real-world problems such as a robot control with visual inputs. Yet learning in such cases is in general difficult, a fact often referred to as the “curse of dimensionality”. In particular, in regression or classification, in order to achieve a certain accuracy algorithms are known to require exponentially many samples in the dimensi...