---Conditional density estimation is an useful alternative to regression to learn an input-output relationship under multi-modality, asymmetry, and heteroscedasticity. The supervised learning method called least-squares conditional density estimation (LSCDE) is the state-of-the-art method that directly estimates the conditional density using a linear model. In this paper, we extend the supervis...

Cast indexing is a very important application for contentbased video browsing and retrieval, since the characters in feature-length films and TV series are always the major focus of interest to the audience. By cast indexing, we can discover the main cast list from long videos and further retrieve the characters of interest and their relevant shots for efficient browsing. This paper proposes a ...

As the information carried in a high spatial resolution image is not represented by single pixels but by meaningful image objects, which include the association of multiple pixels and their mutual relations, the object based method has become one of the most commonly used strategies for the processing of high resolution imagery. This processing comprises two fundamental and critical steps towar...

The mixture of factor analyzers model was first introduced over 20 years ago and, in the meantime, has been extended to several non-Gaussian analogues. In general, these analogues account for situations with heavy tailed and/or skewed clusters. An approach is introduced that unifies many of these approaches into one very general model: the mixture of hidden truncation hyperbolic factor analyzer...

this research was performed to evaluate the potentials of landsat mss data for map-ping land features in arid zones of southeastern esfahan, iran. databases of the area were formed using all available relevant maps and reports which were supported by fieldwork. a supervised image classification approach was used and thirty-two training areas were applied. separability of the spectral classes wa...

This letter investigates a link-analysis variant of discriminant analysis for projecting nodes of a (partially) labeled graph in a low-dimensional subspace and extracting discriminant node features. Basically, it corresponds to a kernel discriminant analysis computed from a kernel on a graph together with a class betweenness measure. As for standard discriminant analysis, the projected nodes ar...

Seismic facies analysis (SFA) aims to classify similar seismic traces based on amplitude, phase, frequency, and other seismic attributes. SFA has proven useful in interpreting seismic data, allowing significant information on subsurface geological structures to be extracted. While facies analysis has been widely investigated through unsupervised-classification-based studies, there are few cases...

In this paper, we systematically study the effect of poorly registered faces on the training and inferring stages of traditional face recognition algorithms. We then propose a novel multiple-instance based subspace learning scheme for face recognition. In this approach, we iteratively update the subspace training instances according to diverse densities, using class-balanced supervised clusteri...

This article introduces a novel approach for sentiment analysis – the clustering-based sentiment analysis approach. By applying a TFIDF weighting method, a voting mechanism and importing term scores, an acceptable and stable clustering result can be obtained. The methodology has competitive advantages over the two existing types of approaches: symbolic techniques and supervised learning methods...