A tool is introduced that uses a novel technique to enable users to explore two-dimensional views of high dimensional gene expression data sets. Unlike other such tools, the interface is intuitive and efficient, allowing the user to easily select views that meet their requirements. The tool is tested on publicly available gene expression data sets and demonstrated to find views that show the se...

For classification problems, feature extraction is a crucial process which aims to find a suitable data representation that increases the performance of the machine learning algorithm. According to the curse of dimensionality [4] theorem, the number of samples needed for a classification task increases exponentially as the number of dimensions (variables, features) increases. On the other hand,...

Similarities and distinctions have been pointed out between ICA and traditional multivariate methods such as factor analysis, principal component analysis and projection pursuit. In this paper, a new important connection between ICA and traditional factor analysis is made. The key of the connection is “factor rotation.”

We propose an object recognition scheme based on a method for feature extraction from gray level images that corresponds to recent statistical theory, called projection pursuit, and is derived from a biologically motivated feature extracting neuron. To evaluate the performance of this method we use a set of very detailed psychophysical 3D object recognition experiments (B ulthoo and Edelman, 19...

The most nongaussian direction to explore the clustering structure of the data is considered to be the interesting linear projection direction by applying projection pursuit. Nongaussianity is often measured by kurtosis, however, kurtosis is well known to be sensitive to influential points/outliers and the projection direction is essentially affected by unusual points. Hence in this paper we fo...

UNLABELLED We present a novel method for finding low-dimensional views of high-dimensional data: Targeted Projection Pursuit. The method proceeds by finding projections of the data that best approximate a target view. Two versions of the method are introduced; one version based on Procrustes analysis and one based on an artificial neural network. These versions are capable of finding orthogonal...

We present a novel method for finding low dimensional views of high dimensional data: Targeted Projection Pursuit. The method proceeds by finding projections of the data that best approximate a target view. Two versions of the method are introduced; one version based on Procrustes analysis and one based on a single layer perceptron. These versions are capable of finding orthogonal or nonorthogo...

The calorific value of coal is an important factor for the economic operation of coal-fired power plant. However, calorific value is tremendous difference between the different coal, and even if coal is from the same mine. Restricted by the coal market, most of coal fired power plants can not burn the designedcoal by now in China. The properties of coal as received are changing so frequently th...

An algorithm for the analysis of multivariate data is presented, and discussed in terms of specific examples. The algorithm seeks to find oneand two-dimensional linear projections of multivariate data that are relatively highly revealing. *Supported by the U.S. Atomic Energy Commission under Contract AT(@+3)515. **Prepared in part in connection with research at Princeton University supported by...

Principal component analysis (PCA) is usually used for compressing information in multivariate data sets by computing orthogonal projections that maximize the amount of data variance. PCA is effective if the multivariate data set is a vector with Gaussian distribution. But multi-spectral images data sets are not probably submitted to such Gaussian distribution. The paper proposes a method based...