speckle is a granular disturbance in coherent images such as synthetic aperture radar (sar) images, modeled as a multiplicative noise. this noise degrades the sar image and complicates the image exploitation using automated image analysis techniques. several approaches have been developed to reduce the effect of speckle noise. recently, the application of compressed sensing (cs) is explored in ...

This paper presents ACE (Autonomous Classification Engine), a framework for using and optimizing classifiers. Given a set of feature vectors, ACE experiments with a variety of classifiers, classifier parameters, classifier ensembles and dimensionality reduction techniques in order to arrive at a good configuration for the problem at hand. In addition to evaluating classification methodologies i...

High dimensionality of the feature space is one of the most important concerns in text classification problems due to processing time and accuracy considerations. Selection of distinctive features is therefore essential for text classification. This study proposes a novel filter based probabilistic feature selection method, namely distinguishing feature selector (DFS), for text classification. ...

This paper proposes to use band selection-based dimensionality reduction (BS-DR) technique in addressing a challenging multi-temporal hyperspectral images change detection (HSI-CD) problem. The aim of this work is to analyze and evaluate in detail the CD performance by selecting the most informative band subset from the original high-dimensional data space. In particular, for cases where ground...

Feature selection is an essential preprocessing step for classifiers with high dimensional training sets. In pattern recognition, feature selection improves the performance of classification by reducing the feature space but preserving the classification capabilities of the original feature space. Image classification using frequent approximate subgraph mining (FASM) is an example where the ben...

The explosive amount of malware continues their threats in network and operating systems. Signature-based method is widely used for detecting malware. Unfortunately, it is unable to determine variant malware on-the-fly. On the hand, behavior-based method can effectively characterize the behaviors of malware. However, it is time-consuming to train and predict for each specific family of malware....

Feature selection is an effective technique in dealing with dimensionality reduction. For classification, it is used to find an “optimal” subset of relevant features such that the overall accuracy of classification is increased while the data size is reduced and the comprehensibility is improved. Feature selection methods contain two important aspects: evaluation of a candidate feature subset a...

Features used for named entity recognition (NER) are often high dimensional in nature. These cause overfitting when training data is not sufficient. Dimensionality reduction leads to performance enhancement in such situations. There are a number of approaches for dimensionality reduction based on feature selection and feature extraction. In this paper we perform a comprehensive and comparative ...

Speed Up Robust Feature (SURF) and Local Contour Sequence(LCS) are methods used for feature extraction techniques for dynamic gesture recognition. A problem presented by these techniques is the large amount of data in the output vector which difficult the classification task. This paper presents a novel method for dimensionality reduction of the features extracted by SURF and LCS, called Convex...