Quality of Quantization and Visualization of Vectors Obtained by Neural Gas and Self-Organizing Map
نویسندگان
چکیده
In this paper, the quality of quantization and visualization of vectors, obtained by vector quantization methods (self-organizing map and neural gas), is investigated. A multidimensional scaling is used for visualization of multidimensional vectors. The quality of quantization is measured by a quantization error. Two numerical measures for proximity preservation (Konig’s topology preservation measure and Spearman’s correlation coefficient) are applied to estimate the quality of visualization. Results of visualization (mapping images) are also presented.
منابع مشابه
NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map
Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different ...
متن کاملThe Time Adaptive Self Organizing Map for Distribution Estimation
The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...
متن کاملCross-entropy embedding of high-dimensional data using the neural gas model
A cross-entropy approach to mapping high-dimensional data into a low-dimensional space embedding is presented. The method allows to project simultaneously the input data and the codebook vectors, obtained with the Neural Gas (NG) quantizer algorithm, into a low-dimensional output space. The aim of this approach is to preserve the relationship defined by the NG neighborhood function for each pai...
متن کاملکاهش رنگ تصاویر با شبکه‌های عصبی خودسامانده چندمرحله‌ای و ویژگی‌های افزونه
Reducing the number of colors in an image while preserving its quality, is of importance in many applications such as image analysis and compression. It also decreases memory and transmission bandwidth requirements. Moreover, classification of image colors is applicable in image segmentation and object detection and separation, as well as producing pseudo-color images. In this paper, the Kohene...
متن کاملIncreasing the Error Tolerance in Transmission of Vector Quantized Images by Self-organizing Map 1. Image Vector Quantization Using Self-organizing Maps
Transmission of Vector Quantized Images by Self-Organizing Map Jari Kangas Helsinki University of Technology Neural Networks Research Centre Rakentajanaukio 2 C, FIN-02150, Espoo, FINLAND tel: +358 0 451 3275, fax: +358 0 451 3277 email: Jari.Kangas@hut. Abstract Image compression is needed for image storage and transmission applications. Vector quantization methods o er good performance when h...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Informatica, Lith. Acad. Sci.
دوره 22 شماره
صفحات -
تاریخ انتشار 2011