Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an ite...
Dick de Ridder, Olga Kouropteva, Oleg Okun, Matti ...
We propose a new algorithm for dimensionality reduction and unsupervised text classification. We use mixture models as underlying process of generating corpus and utilize a novel,...
Data reduction plays an important role in machine learning and pattern recognition with a high-dimensional data. In real-world applications data usually exists with hybrid formats...
Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract usef...
Haiping Lu, Konstantinos N. Plataniotis, Anastasio...
In this paper, we present a tracking framework for capturing articulated human motions in real-time, without the need for attaching markers onto the subject's body. This is a...