Mahalanobis distance

About: Mahalanobis distance is a research topic. Over the lifetime, 4616 publications have been published within this topic receiving 95294 citations.

Rider-Deep-LSTM Network for Hybrid Distance Score-Based Fault Prediction in Analog Circuits

Abstract: Fault prediction in the analog circuits is a serious problem to be addressed on an immediate basis, as traditionally, the faults in the analog circuits are diagnosed only after their occurrence. Since the outcome of the faults creates highly expensive scenarios in case of the analog circuit industry, there is a need for an effective prediction model that keeps track of the faults prior to their occurrence. Accordingly, this article focuses on the fault prediction model in analog circuits using proposed deep model called, Rider-deep-long short-term memory (LSTM). Here, the significance and precision of the prediction relies on the fault indicator, which is computed based on three distance measures, such as mahalanobis distance, Euclidean distance, and angular distance, and thereby, enables an effective health estimation of the circuit. The estimation is effectively solved using the Rider-deep-LSTM, which is the integration of proposed Rider-Adam algorithm in deep-LSTM, for training the model parameters. The proposed prediction method acquires the Pearson correlation coefficient of 0.9973 and 0.9919 while using the circuits, such as solar power converter and low noise bipolar transistor amplifier.

The problem of classification and distance between two populations.

Abstract: The problem of specifying an individual as a member of one of many populations, and the classification of a number of populations themselves in some significant system based on the configuration of various characteristics, are of great importance in anthropological and biological research. We may find a collection of skulls with unspecified sexes, and the problem faced by an anthropologist is assigning proper sex to a skull. Judgment based on mere anatomical appreciation of a skull may not be altogether wrong, but is subject to criticism especially when objective methods are available. This problem has been solved by Fisher's discriminant function. If we have a collection of skulls grouped according to specified populations, the problem is to arrive at constellations of populations such that any two members of a constellation are closer to one another than any two belonging to different constellations. This problem can be solved by Mahalanobis's generalized distance.

A study on combining image representations for image classification and retrieval

Abstract: A flexible description of images is offered by a cloud of points in a feature space. In the context of image retrieval such clouds can be represented in a number of ways. Two approaches are here considered. The first approach is based on the assumption of a normal distribution, hence homogeneous clouds, while the second one focuses on the boundary description, which is more suitable for multimodal clouds. The images are then compared either by using the Mahalanobis distance or by the support vector data description (SVDD), respectively. The paper investigates some possibilities of combining the image clouds based on the idea that responses of several cloud descriptions may convey a pattern, specific for semantically similar images. A ranking of image dissimilarities is used as a comparison for two image databases targeting image classification and retrieval problems. We show that combining of the SVDD descriptions improves the retrieval performance with respect to ranking, on the contrary to the Mahalanobis case. Surprisingly, it turns out that the ranking of the Mahalanobis distances works well also for inhomogeneous images.