A new approach of constructing the similarity matrix for eigendecomposition on graph Laplacians is proposed. We first connect the Locality Preserving Projection method to probability density derivatives, which are then replaced by informative score vectors. This change yields a normalization factor and increases the contribution of the data pairs in low-density regions. The proposed method can ...

Microarray experiments are emerging as one of the main driving forces in modern biology. By allowing the simultaneous monitoring of the expression of the entire genome for a given organism, array experiments provide tremendous insight into the fundamental biological processes that translate genetic information. One of the major challenges is to identify computationally efficient and biologicall...

We introduce a probabilistic model that generalizes classical linear discriminant analysis and gives an interpretation for the components as informative or relevant components of data. The components maximize the predictability of class distribution which is asymptotically equivalent to (i) maximizing mutual information with the classes, and (ii) finding principal components in the so-called le...

We create a formal framework for the design of informative securities in prediction markets. These securities allow a market organizer to infer the likelihood of events of interest as well as if he knew all of the traders’ private signals. We consider the design of markets that are always informative, markets that are informative for a particular signal structure of the participants, and inform...

a generalized heckman model is used for the joint modeling of longitudinal continuousresponses and dropout in order to see the influence of a small perturbation of the elements of thecovariance structure on displacement of the likelihood. the perturbation from random dropout in thedirection of informative dropout is considered for mastitis data.

The goal of pattern classification can be approached from two points of view: informative where the classifier learns the class densities, or discriminative where the focus is on learning the class boundaries without regard to the underlying class densities. We review and synthesize the tradeoffs between these two approaches for simple classifiers, and extend the results to modern techniques su...