In this paper, we investigate and systematically evaluate two machine learning algorithms for analog fault detection and isolation: (1) Restricted Coloumb Energy (RCE) Neural Network, and (2) Learning Vector Quantization (LVQ). The RCE and LVQ models excel at recognition and classiication types of problems. In order to evaluate the eecacy of the two learning algorithms, we have developed a soft...

This paper presents an algorithm that jointly optimizes a lattice vector quantizer (LVQ) and an entropy coder in a subband coding at all ranges of bit rate. Estimation formulas for both entropy and distortion of lattice quantized subband images are derived. From these estimates, we then develop dynamic algorithm optimizing the LVQ and entropy coder together for a given entropy rate. Compared to...

We present a new way to take advantage of the dis-criminative power of Learning Vector Quantization in combination with continuous density hidden Markov models. This is based on viewing LVQ as a non-linear feature transformation. Class-wise quantization errors of LVQ are modeled by continuous density HMMs, whereas the practice in the literature regarding LVQ/HMM hybrids is to use LVQ-codebooks ...

Prototype-based classification models, and particularly Learning Vector Quantization (LVQ) frameworks with adaptive metrics, are powerful supervised classification techniques with good generalization behaviour. This thesis proposes three advanced learning methodologies, in the context of LVQ, aiming at better classification performance under various classification settings. The first contributi...

This contribution extends generalized LVQ, generalized relevance LVQ, and robust soft LVQ to the graph domain. The proposed approaches are based on the basic learning graph quantization (lgq) algorithm using the orbifold framework. Experiments on three data sets show that the proposed approaches outperform lgq and lgq2.1.

Learning Vector Quantization (LVQ) are popular multi-class classification algorithms. Prototypes in an LVQ system represent the typical features of classes in the data. Frequently multiple prototypes are employed for a class to improve the representation of variations within the class and the generalization ability. In this paper, we investigate the dynamics of LVQ in an exact mathematical way,...

This article presents a new computational intelligence technique for pattern recognition of graphic elements (e.g. event-related potential, auditory evoked potential, kcomplex, spindle) embedded in electro-encephalographic signals. More precisely, we have extended the learning vector quantization (LVQ) algorithm by Kohonen to nonidentity assignment to robustly detect evoked potentials in a nois...

This paper presents the development and investigates the properties of ordered weighted learning vector quantization (LVQ) and clustering algorithms. These algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators. An axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functi...

Two methods to overcome the problems with large vector quantization (VQ) codebooks are lattice VQ (LVQ) and product codes. The approach described in this paper takes advantage of both methods by applying residual VQ with LVQ at all stages. Using LVQ in conjunction with entropy coding is strongly motivated by the fact that entropy constrained but structurally unconstrained VQ design leads to mor...