Informational Energy Kernel for LVQ
نویسندگان
چکیده
We describe a kernel method which uses the maximization of Onicescu’s informational energy as a criteria for computing the relevances of input features. This adaptive relevance determination is used in combination with the neural-gas and the generalized relevance LVQ algorithms. Our quadratic optimization function, as an L type method, leads to linear gradient and thus easier computation. We obtain an approximation formula similar to the mutual information based method, but in a more simple way.
منابع مشابه
An Informational Energy Approach to Feature Selection
In this work, we focus on machine learning methods for handling data sets containing large amounts of irrelevant information. We address two key issues: the problem of selecting relevant features, and the problem of weighting (ranking) these features. We describe our Energy Supervised Relevance Neural Gas (ESRNG) algorithm, a kernel method which uses the maximization of Onicescu’s informational...
متن کاملAn informational energy LVQ approach for feature ranking
Input feature ranking and selection represent a necessary preprocessing stage in classification, especially when one is required to manage large quantities of data. We introduce a weighted LVQ algorithm, called Energy Relevance LVQ (ERLVQ), based on Onicescu’s informational energy [10]. ERLVQ is an incremental learning algorithm for supervised classification and feature ranking.
متن کاملA new two-step learning vector quantization algorithm for image compression
The learning vector quantization (LVQ) algorithm is widely used in image compression because of its intuitively clear learning process and simple implementation. However, LVQ strongly depends on the initialization of the codebook and often converges to local optimal results. To address the issues, a new two-step LVQ (TsLVQ) algorithm is proposed in the paper. TsLVQ uses a correcting learning st...
متن کاملUsing PCA with LVQ, RBF, MLP, SOM and Continuous Wavelet Transform for Fault Diagnosis of Gearboxes
A new method based on principal component analysis (PCA) and artificial neural networks (ANN) is proposed for fault diagnosis of gearboxes. Firstly the six different base wavelets are considered, in which three are from real valued and other three from complex valued. Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared...
متن کاملFuzzy-Kernel Learning Vector Quantization
This paper presents an unsupervised fuzzy-kernel learning vector quantization algorithm called FKLVQ. FKLVQ is a batch type of clustering learning network by fusing the batch learning, fuzzy membership functions, and kernel-induced distance measures. We compare FKLVQ with the wellknown fuzzy LVQ and the recently proposed fuzzy-soft LVQ on some artificial and real data sets. Experimental results...
متن کامل