Showing papers on "Dimensionality reduction published in 1988"

Auto-association by multilayer perceptrons and singular value decomposition

Abstract: The multilayer perceptron, when working in auto-association mode, is sometimes considered as an interesting candidate to perform data compression or dimensionality reduction of the feature space in information processing applications. The present paper shows that, for auto-association, the nonlinearities of the hidden units are useless and that the optimal parameter values can be derived directly by purely linear techniques relying on singular value decomposition and low rank matrix approximation, similar in spirit to the well-known Karhunen-Loeve transform. This approach appears thus as an efficient alternative to the general error back-propagation algorithm commonly used for training multilayer perceptrons. Moreover, it also gives a clear interpretation of the role of the different parameters.

Component and correspondence analysis: dimension reduction by functional approximation

Abstract: Contributions to this work begin from a homogeneous-analytic point of view, then go further to analyze continuous variables, extending the geometrical approach of Gifi, and applying functional analytic techniques to problems involving replicated time series data (which are not subject to classical correspondence analysis and principal component analysis). Closing chapters address probability coding (which is related to fuzzy coding), and two approaches to component analysis: optimal scaling (which uses montone splines) embedded in a more classical statistical framework; and the connection between explorative multivariate data analysis and confirmation analysis based on statistical modelling.

Feature extraction in the Neocognitron

Abstract: The authors present theoretical and numerical developments in the understanding of feature extraction in the Neocognitron. First, they show that the feature extraction process is equivalent to a generalized nonlinear discriminant. Second, they show that the operation of the feature-extraction process can be linked to the eigenvectors and eigenvalues of a matrix comprised of the excitatory and inhibitory convolution masks. Third, the authors show how the choice of parameters for the feature extraction and learning process affects the feature extraction capabilities of the machine. >

Dimensionality Reduction In High Resolution Direction Of Arrival Estimation

Abstract: Reducing data dimension prior to application of direction of arrival estimation algorithms is shown to lower computational requirements and improve certain aspects of performance. In this paper we utilize linear transformations for mapping full dimension data into a lower dimensional space. The transformation is designed to maximize the average signal to noise ratio over a set of likely signal scenarios. Reduced dimension versions of the MUSIC and minimum norm algorithms are presented and discussed. An example illustrates the effectiveness of the method.

Electrical Power System Dynamic Stability Evaluation using a Knowledge-Based Approach

Abstract: In this paper, dynamic stability assessment of electrical power systems is formulated as a pattern recognition problem so that it is fast and accurate enough for on-line applications. The problem concerned is the potentially hazardous dynamic instability of the interconnected power system between Hong Kong and China. New techniques, such as the Branch and Bound Search, are employed to achieve optimality in Feature Selection. In Feature Extraction where further dimensionality reduction is made, matrix augmentation is used to extract information content in class-centralised vectors as well as in class-mean vectors. Finally, for improvement of classification efficiency, an innovative approach making use of partitioning and decision tree search is pursued to tackle the non- linearly separable patterns which exist uniquely in the study of dynamic stability.

Evaluation using a knowledge-based approach

Abstract: In this paper, dynamic stabili ty assessment of electrical power systems is formulated as a pattern recognition problem so that it is fast and aCCllrate enough for on-line applications. The problem concerned is the potentially hazardous dynamic instability of the interconnected power system between Hong Kong and China. New techniques, such as the Branch and Bound Search, are employed to achieve optimality in Feature Selection. In .Feature Extraction where further dimensionality reduction is made, matrix augmentation is used to extract