Add to ORKGA recent trend in exemplar based unsupervised learning is to formulate the learning problem as a convex optimization problem. Convexity is achieved by restricting the set of possible prototypes to training exemplars. In particular, this has been done for clustering, vector quantization and mixture model density estimation. In this paper we propose a novel algorithm that is theoretically and practically superior to these convex formulations. This is possible by posing the unsupervised learning problem as a single convex master problem" with non-convex subproblems. We show that for the above learning tasks the subproblems are extremely wellbehaved and can be solved efficiently
In this paper, an algorithm is proposed to integrate the unsupervised learning with the optimization...
Robust model fitting is a core algorithm in a large number of computer vision applications. Solving ...
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how wit...
A recent trend in exemplar based unsupervised learning is to formulate the learning problem as a con...
Address email Clustering is often formulated as the maximum likelihood estimation of a mixture model...
Unsupervised learning - i.e., learning with unlabeled data - is increasingly important given today\u...
Exemplar-based clustering methods have been extensively shown to be effective in many clustering pro...
In the last decade, machine learning algorithms have been substantially developed and they have gain...
Most recent unsupervised learning methods explore alternative objectives, often referred to as self-...
Unsupervised and semi-supervised learning are explored in convex clustering with metric learning whi...
In this paper, we introduce new algorithms that perform clustering and feature weighting simultaneou...
Learning from multi-view data is important in many applications. In this paper, we propose a novel c...
AbstractIn this paper, an algorithm is proposed to integrate the unsupervised learning with the opti...
Learning from multi-view data is important in many applications. In this paper, we propose a novel c...
Nebel D, Hammer B, Frohberg K, Villmann T. Median variants of learning vector quantization for learn...
In this paper, an algorithm is proposed to integrate the unsupervised learning with the optimization...
Robust model fitting is a core algorithm in a large number of computer vision applications. Solving ...
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how wit...
A recent trend in exemplar based unsupervised learning is to formulate the learning problem as a con...
Address email Clustering is often formulated as the maximum likelihood estimation of a mixture model...
Unsupervised learning - i.e., learning with unlabeled data - is increasingly important given today\u...
Exemplar-based clustering methods have been extensively shown to be effective in many clustering pro...
In the last decade, machine learning algorithms have been substantially developed and they have gain...
Most recent unsupervised learning methods explore alternative objectives, often referred to as self-...
Unsupervised and semi-supervised learning are explored in convex clustering with metric learning whi...
In this paper, we introduce new algorithms that perform clustering and feature weighting simultaneou...
Learning from multi-view data is important in many applications. In this paper, we propose a novel c...
AbstractIn this paper, an algorithm is proposed to integrate the unsupervised learning with the opti...
Learning from multi-view data is important in many applications. In this paper, we propose a novel c...
Nebel D, Hammer B, Frohberg K, Villmann T. Median variants of learning vector quantization for learn...
In this paper, an algorithm is proposed to integrate the unsupervised learning with the optimization...
Robust model fitting is a core algorithm in a large number of computer vision applications. Solving ...
This book summarizes the state-of-the-art in unsupervised learning. The contributors discuss how wit...