This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The
identification of outliers can lead to the discovery of truly unexpected knowledge in areas …

We present an unsupervised technique for visual learning, which is based on density
estimation in high-dimensional spaces using an eigenspace decomposition. Two types of …

We propose a new technique for direct visual matching of images for the purposes of face
recognition and image retrieval, using a probabilistic measure of similarity, based primarily …

We present a method to learn object class models from unlabeled and unsegmented
cluttered scenes for the purpose of visual object recognition. We focus on a particular type of …

Abstract Data Mining and knowledge Discovery in Databases (KDD) promise to play an
important role in the way people interact with databases, especially decision support …

In remote sensing applications" ground-truth" data is often used as the basis for training
pattern recognition algorithms to gener (cid: 173) ate thematic maps or to detect objects of …

Databases are growing in size to a stage where traditional techniques for analysis and
visualization of the data are breaking down. Data mining and knowledge discovery in …

Investigates the use of linear and nonlinear principal manifolds for learning low-dimensional
representations for visual recognition. Several leading techniques-principal component …

Many object classes, including human faces, can be modeled as a set of characteristic parts
arranged in a variable spatial configuration. We introduce a simplified model of a …

Data mining and knowledge discovery in databases (KDD) promise to play an important role
in the way people interact with databases, especially scientific databases where analysis …