A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery (2008)
In recent work by (Song et al., 2007), it has been proposed to perform clustering by maximizing a Hilbert-Schmidt independence criterion with respect to a predefined cluster structure Y , by solving...
Correlational Spectral Clustering (2008)
We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common in real world data...
Learning to Localize Objects with Structured Output Regression (2008)
Blaschko, M.B., Lampert, C.H., Forsyth, D. A., Torr, P. H.S., Zisserman, A.
Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to the localization...
Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis (2008)
Blaschko, M.B., Lampert, C.H., Gretton, A., Daelemans, W., Goethals, B., Morik, K.
Kernel canonical correlation analysis (KCCA) is a dimensionality reduction technique for paired data. By finding directions that maximize correlation, KCCA learns representations that are more...
Beyond Sliding Windows: Object Localization by Efficient Subwindow Search (2008)
Lampert, C.H., Blaschko, M.B., Hofmann, T.
Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To perform...
A Multiple Kernel Learning Approach to Joint Multi-Class Object Detection (2008)
Lampert, C., Blaschko, M.B., Rigoll, G.
Most current methods for multi-class object classification and localization work as independent 1-vs-rest classifiers. They decide whether and where an object is visible in an image purely on a...
DOI 10.1007/s10994-009-5111-0 Structured prediction by joint kernel support estimation (2008)
Abstract Discriminative techniques, such as conditional random fields (CRFs) or structure aware maximum-margin techniques (maximum margin Markov networks (M 3 N), structured output support vector...
Conformal Multi-Instance Kernels (2006)
In the multiple instance learning setting, each observation is a bag of feature vectors of which one or more vectors indicates membership in a class. The primary task is to identify if any vectors in...