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With the arrival of the era of big data, people's ability to collect and obtain data is becoming more powerful. These data have shown the characteristics of high dimension, large scale and complex structure. High dimensional data has seriously hindered the efficiency of data mining algorithm, we call it "the Dimension disaster ". Therefore, dimension reduction technology has become the primary...
Accurate feature extraction plays a vital role in the fields of machine learning, pattern recognition and image processing. Feature extraction methods based on principal component analysis (PCA), independent component analysis (ICA), and linear discriminant analysis (LDA) are capable of improving the performances of classifiers. In this paper, we propose two features extraction approaches, which integrate...
In this paper, an image segmentation method is presented to analyze the clusters of Computed Tomography (CT) image. Target image is divided to small parts called as observation screens. Principal Component Analysis (PCA) is used for better representation of features about observation screens. The optimal number of component related with observation screen is determined by Horn's Parallel Analysis...
The intrusion detection rate is greatly influenced by the parameters of the support vector machine (SVM) model. In order to overcome the parameter limits to improve the identify accuracy of Distributed Denial of Service (DDoS) attack, this paper presents a new detection method based on Kernel Principle Component Analysis (KPCA) and Particle Swarm Optimization (PSO)-Support Vector Machine (SVM). The...
Sequential fuzzy co-cluster extraction has been proven to be useful for collaborative filtering tasks by extracting user-item co-clusters, in which promising items are connected to the corresponding users in each co-cluster. Since some popular items can be shared by multiple clusters in collaborative filtering problems, exclusive conditions, which force objects to belong to only one cluster, were...
In this paper, we construct a unified framework for dimensionality reduction, for simplicity we call it essential kernel principal component analysis (EKPCA). Some of well-known dimensionality reduction methods, such as kernel principal component analysis, locally linear embedding, Laplacian eigenmaps, Isomaps, diffusion maps are subject to this framework.
Kernel Independent Component Analysis (KICA) which is advanced recently is a non-linear method for blind source separation (BSS). KICA can't reduce the dimension of multidimensional data when extract its feature, that is to say, KICA can't remove the disturbing noise in observed sample signal. For these reason, paper improved its ability to process the multidimensional data, recurring to the characteristic...
The aim of this study is to evaluate the effectiveness of decision tree as classifier for recognition of four main human postures (standing, sitting, bending and lying) since decision trees are well known for their success for prediction, recognition and classification task in data mining problems. Firstly, the eigenfeatures of these postures are optimized via Principal Component Analysis rules of...
Video summarization is an efficient and flexible way to represent video data. In this paper, we use the kernel PCA and clustering based key frame extraction to realize multilevel video representation. In order to remove the redundancy caused by large scene changes, SIFT flow scene alignment is performed on the clustering set of key frames. After alignment, one representative frame is chosen from the...
In this paper, a novel approach for the recognition of weed seeds, known as color principal component analysis (PCA), is presented. This experiment involves two steps: dimensionality reduction with color PCA and classification. In dimensionality reduction part, color is used as an important element to identify weed seeds. To perform the recognition of color weed seeds images, we use the features of...
Feature extraction is the transformation of high-dimensional data into a meaningful representation of reduced dimensionality. The representation extracted are often beneficial to mitigate the computational complexity and improve the accuracy of a particular classifier. In this paper we introduce a novel feature extraction algorithm called K nearest neighbor local margin maximization and apply it to...
This paper develops a supervised discriminant technique, called margin maximum embedding discriminant (MMED), for dimensionality reduction of high-dimensional data. In graph embedding, our objective is to find a linear transform matrix to make the samples in the same class as compact as possible and the samples belong to the different classes as dispersed as possible. The proposed method effectively...
A new feature extraction method based on immune clonal selection (ICSA) and PCA is proposed for classification of hyperspectral remote sensing image. As the hyperspectral remote sensing image is acquired in very narrow spectral channels, the resulting high-dimensional feature sets may contain redundant information. Therefore, feature extraction is necessary to classify a data with large dimension...
A novel automatic real-time video parsing framework for gradual transition based on principal component analysis is presented. In this framework, three core factors of video parsing that is feature space, discontinuity metric and decision rule are stressed to improve the parsing performance. Through this PCA-based framework, the negative influence of significant camera or objects movement to the gradual...
In this paper, we provide a method to recognize weed seeds based on computer vision. According to the stability and genetic characteristics described in phytotaxonomy for weed seeds. Image processing method encompasses threshold segmentation and smooth processing etc, nine features parameters are extracted by image processing, which keep RST invariance. The principal components analysis method is...
A number of Intrusion Detection Systems (IDS) research efforts have demonstrated that network-based attacks can be detected by modeling normal network packet payloads and watching for anomalies. In this paper, we explore a data mining technique based on Principal Component Analysis that can identify specific features within packet payloads that are highly representative of the network traffic. of...
Principal component analysis (PCA) is an efficient feature extraction method which reduces the dimensions of the feature vectors and removes the correlation among them, with little loss of information, by projecting the original feature space into a small subspace through a transformation. However, it requires a larger amount of training data when calculate the full covariance matrix of each speaker...
Kernel principal component analysis (kernel PCA or KPCA) has been used widely for non-linear feature extraction, dimensionally reduction, and classification problems. However, KPCA is known to have high computational complexity, that is the eigenvalue decomposition of which size equals to the number of samples n. Moreover, in order to calculate projection of vector onto the subspace obtained by KPCA,...
Artificial neural networks have been widely used for knowledge extraction from biomedical datasets and constitute an important role in bio-data exploration and analysis. In this work, we proposed a new curvilinear algorithm for training large neural networks which is based on the analysis of the eigenstructure of the memoryless BFGS matrices. The proposed method preserves the strong convergence properties...
In this study, PCA (principal component analysis) was used to select features and eliminate the redundancy features in process of rolling bearing fault monitoring. And then a new method was mentioned out to optimize the feature space with P-PCA (parts principal component analysis), which needs to deal with the data of each fault categories with PCA firstly, and then reconstructed the feature space...
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