Projection Pursuit Constructive Neural Networks Based on Quality of Projected Clusters
نویسندگان
چکیده
Linear projection pursuit index measuring quality of projected clusters (QPC) is used to discover non-local clusters in high-dimensional multiclass data, reduction of dimensionality, feature selection, visualization of data and classification. Constructive neural networks that optimize the QPC index are able to discover simplest models of complex data, solving problems that standard networks based on error minimization are not able to handle. Tests on problems with complex Boolean logic and a few real world datasets show high efficiency of this
منابع مشابه
Fast Projection Pursuit Based on Quality of Projected Clusters
Projection pursuit index measuring quality of projected clusters (QPC) introduced recently optimizes projection directions by minimizing leave-one-out error searching for pure localized clusters. QPC index has been used in constructive neural networks to discover non-local clusters in high-dimensional multiclass data, reduce dimensionality, aggregate features, visualize and classify data. Howev...
متن کاملConstructive Neural Network Algorithms That Solve Highly Non-separable Problems
Learning from data with complex non-local relations and multimodal class distribution for widely used classification algorithms is still very hard. Even if accurate solution is found the resulting model may be too complex for a given data and will not generalize well. New types of learning algorithms are needed to extend capabilities of standard machine learning systems. Projection pursuit meth...
متن کاملLow Bit Rate Image Compression with Orthogonal Projection Pursuit Neural Networks
A new multiresolution algorithm for image compression based on projection pursuit neural networks is presented. High quality low bit-rate image compression is achieved first by segmenting an image into regions of different sizes based on perceptual variation in each region and then constructing a distinct code for each block by using the orthogonal projection pursuit neural networks. This algor...
متن کاملImplementing projection pursuit learning
This paper examines the implementation of projection pursuit regression (PPR) in the context of machine learning and neural networks. We propose a parametric PPR with direct training which achieves improved training speed and accuracy when compared with nonparametric PPR. Analysis and simulations are done for heuristics to choose good initial projection directions. A comparison of a projection ...
متن کاملCombining Exploratory Projection Pursuit and Projection Pursuit Regression with Application to Neural Networks
Parameter estimation becomes difficult in high-dimensional spaces due to the increasing sparseness of the data. Therefore, when a low-dimensional representation is embedded in the data, dimensionality reduction methods become useful. One such method-projection pursuit regression (Friedman and Stuetzle 1981 (PPR)-is capable of performing dimensionality reduction by composition, namely, it constr...
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:
دوره شماره
صفحات -
تاریخ انتشار 2008