一种融合T-Rank和Softmax的特征提取算法研究

High dimensional data clustering is a difficult task due to the curse of dimensionality. Traditional clustering methods usually fail to produce meaningful results for high dimensional data. Hypergraph partition is believed to be a promising method for dealing with this challenge. In this work, a new high dimensional clustering method called Merging Dense SubGraphs (MDSG) is proposed. A graph G is first constructed from the data by defining an adjacency relationship between the data points using Shared k Nearest Neighbors (SNN). Then a hypergraph is created from the graph G by defining the hyperedges to be all the maximal cliques in the graph. After the hypergraph is produced, an improved hypergraph partitioning method is used to produce the final clustering results. The proposed MDSG method is evaluated on several real high dimensional handwritten datasets, and the experimental results show that the proposed method is superior to the traditional clustering method and other hypergraph partition methods for high dimensional handwritten data clustering.

DBFS: An effective Density Based Feature Selection scheme for small sample size and high dimensional imbalanced data sets

Metric trees are designed for improving efficiency of similarity search in high dimensional data. Searching a metric tree always terminates at leaf nodes, restricting the hierarchical search to a certain leaf level, and the linear search in leafs to a particular leaf size. This may result in performing additional unneeded distance comparisons, which increases the search time as the number of dimensions increases. It is proposed that the search adapts itself to the query in question, so as to avoid unneeded distance comparisons. Thus, the hierarchical search should terminate when no more search- space pruning can be useful, and search then continues linearly through the un-pruned data space. Hierarchical search can thus stop any where above or below the original leaf level of a metric tree. Search rules are proposed to adapt the search to the query parameters. A modification to the metric tree is also suggested to adopt the proposed rules. High dimensional gene expression data sets are used to evaluate the new algorithm, showing speed ups of 55% compared to traditional search.

Recently, anomaly detection has acquired a realistic response from data mining scientists as a graph of its reputation has increased smoothly in various practical domains like product marketing, fraud detection, medical diagnosis, fault detection and so many other fields. High dimensional data subjected to outlier detection poses exceptional challenges for data mining experts and it is because of natural problems of the curse of dimensionality and resemblance of distant and adjoining points. Traditional algorithms and techniques were experimented on full feature space regarding outlier detection. Customary methodologies concentrate largely on low dimensional data and hence show ineffectiveness while discovering anomalies in a data set comprised of a high number of dimensions. It becomes a very difficult and tiresome job to dig out anomalies present in high dimensional data set when all subsets of projections need to be explored. All data points in high dimensional data behave like similar observations because of its intrinsic feature i.e., the distance between observations approaches to zero as the number of dimensions extends towards infinity. This research work proposes a novel technique that explores deviation among all data points and embeds its findings inside well established density-based techniques. This is a state of art technique as it gives a new breadth of research towards resolving inherent problems of high dimensional data where outliers reside within clusters having different densities. A high dimensional dataset from UCI Machine Learning Repository is chosen to test the proposed technique and then its results are compared with that of density-based techniques to evaluate its efficiency.

Background/Objectives: This paper aims to develop a novel intelligent recommender system suitable for high dimensional data where multiple factor variables influence on multiple response variables. Methods/Statistical Analysis: This paper suggests that the structure of the cause-and-effect relations among the variables can be represented in a simple form called structured association network model (SANM). Based on the SANM and conventional data mining techniques such as association rule mining and naive Bayesian classifier, the proposed recommender system computes three novel recommendation scores for each response variables, and the variables with high scores can be selected for recommendation. Findings: For illustration, the proposed recommender system has been applied to a mass health examination result data set. Owing to its simple structure, a SANM for a given data set can be easily obtained by simply identifying the factor and the response variables, and the experiment results revealed that the proposed system can identify the recommendable items for the individuals more effectively than the traditional classification techniques such as naive Bayesian classifier. Consequently, we can conclude that the proposed recommender system can deal with high dimensional transaction data set in more effective manner than the traditional approaches where the underlying semantic relationships among the variables are not considered. Applications/Improvements: The proposed recommender system is useful for evaluation of the potential risks of specific diseases. Moreover, the recommendation scores can also be used as a tool for feature construction.

Outlier detection aims to find a data sample that is significantly different from other data samples. Various outlier detection methods have been proposed and have been shown to be able to detect anomalies in many practical problems. However, in high dimensional data, conventional outlier detection methods often behave unexpectedly due to a phenomenon called the curse of dimensionality. In this paper, we compare and analyze outlier detection performance in various experimental settings, focusing on text data with dimensions typically in the tens of thousands. Experimental setups were simulated to compare the performance of outlier detection methods in unsupervised versus semi-supervised mode and uni-modal versus multi-modal data distributions. The performance of outlier detection methods based on dimension reduction is compared, and a discussion on using k-NN distance in high dimensional data is also provided. Analysis through experimental comparison in various environments can provide insights into the application of outlier detection methods in high dimensional data.

Regarding the magnificent ability of the fuzzy logic in explanation and analyzing the real phenomena, using fuzzy data is growing rapidly. But the tools for doing data mining in a fuzzy data set; especially in high dimension, are not sufficient. Considering the remarkable ability of the LoOP (Local Outlier Probabilities) method in outlier detecting in high dimensional crisp data set, like supplying for each data object, determining outlier probability as scar which is easily explainable competitor on various synthetic and real world data sets, the aim of this article is to present an extended version of this method for using in high dimensional fuzzy database. In order to provide a completely fuzzy strategy, first we propose a new approach to assigning distance between fuzzy high dimension data based on Hausdorff and Vertex distance. After proving that this distance measuring is a metric, since LoOP is a distance based method, we redefined the LoOP algorithm step by step based on the new meter. Then we allocate an outlier score in the range of [0,1] to each data based on LoOP idea, to identify the outliers. The presented numerical tests show the efficiency of the new method, for random cases of knowing and unknowing the outlier and a real case. The results shows also the stability and robustness of the presented method more that its accuracy.

Fuzzy partition based soft subspace clustering and its applications in high dimensional data

In order to solve the problem of low accuracy of time dimension feature extraction and classification of high-dimensional large data streams, this paper proposes a time dimension feature extraction and classification algorithm of high-dimensional large data streams based on unsupervised learning. Analyze the trend of high-dimensional data flow changes under machine learning, and achieve dimensionality reduction of high-dimensional large traffic time dimensional data through local save projection. Analyze the spatial relationship between feature attributes and feature space, segment and fit high-dimensional big data streams and time dimensional feature data streams, further segment time dimensional sequences using sliding windows, and complete feature extraction through discrete dyadic wavelet transform. According to the clustering algorithm, cluster the time dimension feature data stream, calculate the cosine similarity of the feature data, model the time dimension feature stream of training samples, use the feature classification function to minimize the classification loss, and use unsupervised learning to achieve the final classification task. The test results show that this method can improve the temporal feature extraction and classification accuracy streams.

High dimension and complexity of network high-dimensional data lead to poor feature selection effect network high-dimensional data. To effectively solve this problem, feature selection algorithms for high-dimensional network data based on supervised discriminant projection (SDP) have been designed. The sparse representation problem of high-dimensional network data is transformed into an Lp norm optimization problem, and the sparse subspace clustering method is used to cluster high-dimensional network data. Dimensionless processing is carried out for the clustering processing results. Based on the linear projection matrix and the best transformation matrix, the dimensionless processing results are reduced by combining the SDP. The sparse constraint method is used to achieve feature selection of high-dimensional data in the network, and the relevant feature selection results are obtained. The experimental findings demonstrate that the suggested algorithm can effectively cluster seven different types of data and converges when the number of iterations approaches 24. The F1 value, recall, and precision are all kept at high levels. High-dimensional network data feature selection accuracy on average is 96.9%, and feature selection time on average is 65.1 milliseconds. The selection effect for network high-dimensional data features is good.

Because of the poor performance of the Random Forest algorithm in processing the classification of high-dimensional unbalanced data, a Hybrid Samping&Feature Selection Random Forest optimization strategy (Hybrid Samping&Feature Selection Random Forest (HF_RF) is proposed in this paper. First, from the data level, the high-dimensional unbalanced data set is preprocessed by SMOTE algorithm combined with random undersampling to achieve balanced unbalanced data. At the same time, the clustering algorithm is combined with SMOTE algorithm to improve the processing ability of the algorithm for negative samples; On the algorithm level, through the Relief F algorithm, different weight values are given to the preprocessed high-dimensional data, irrelevant and redundant features are eliminated, and high-dimensional data is reduced for dimensionality; Finally, the weighted voting principle is used to further elevate the predictive performance of HF_RF. The experimental results show that compared with the traditional algorithm, the proposed algorithm has higher indicators when dealing with high-dimensional unbalanced data, which proves that the HF_RF proposed in this paper is The correctness of the algorithm and its effectiveness in improving the classification performance of high-dimensional unbalanced data.

There are a large number of high-dimensional data in the Internet of Brain Things, and the data is reduced from the high-dimensional to low-dimensional to maintain the similarity between the data, thereby effectively ensuring the operating speed of the Internet of Brain Things. In the traditional method, the distributed dimensionality reduction reconstruction algorithm of the High dimensional data has poor dimensionality reduction effect and serious data distortion after reconstruction. A method for dimensionality reconstruction of high-dimensional data in the Internet of Brain Things is proposed. By using the algorithm of linear discriminant analysis, the projection matrix of high dimensional data is constructed to solve it. According to the solution results, using improved implicit variable model to establish a high dimensional large data dimensionality reduction model for the Internet of Brain Things. The fitness value of data after dimensionality reduction is calculated by quantum immune clonal algorithm, and the optimal individual and optimal solution are determined. The data reconfiguration is realized through the optimal solution marshalling. Experimental results show that the proposed algorithm can effectively improve the dimensionality reduction of High dimensional data in the Internet of Brain Things. After reconstruction, the reconstructed data retain accurate data information, the reliability of reconstructed data is high, and the computational complexity is not high, the need for small storage space, and the advantages of strong promotion ability.

In recent year, stock investor in Indonesia increased rapidly, so it is required to do analysis about the stock that helps the investor in their investment plan. Clustering is beneficial to select the appropriate stock for investors. Unfortunately, stock prices keep varying from time to time. Consequently, it is not an easy work to select the stock for investment. In addition, stock price time series data are high dimensional data that influenced by many factors. In this study, high dimensional data are obtained by the time frame of each factor. Therefore, it is important to use a suitable technique to cluster high dimensional data. This paper presents High Dimensional Data Clustering (HDDC), a model-based clustering based on Gaussian Mixture Model, using the Expectation-Maximization (EM) algorithm. HDDC via EM algorithm gives a more robust result, and it possible to make an additional assumption. Moreover, this paper combines a high-dimensional clustering technique HDDC via EM algorithm and the most popular feature extraction technique Principal Component Analysis (PCA). This paper comparing methods of clustering technique HDDC and the combination between HDDC and PCA to know the most effective method which gives better result in clustering high-dimensional time series data. The 155 data features are reduced to 7 principal components using PCA analysis. Despite PCA has increased the time efficiency of building the model, clustering technique HDDC via EM algorithm enables to handle the high-dimensional data better than the combination with PCA.

Pavement distress images are typical high dimensional nonlinear data. Manifold learning algorithms can find the intrinsic characteristic hidden in the distress images, which helps to better recognize them. Unlike most of manifold learning algorithms, multilayer autoencoders have solved the data reconstructed problem through building a bi-directional mapping between the high dimensional data and the low dimensional data. An automatic pavement distress image recognition method based on multilayer autoencoders was proposed, which combined the image processing method and multilayer autoencoders. In the method, the distress images were firstly processed with the image processing method. Then the images were reduced dimensions and reconstructed with multilayer autoencoders. Lastly, the distress type was recognized through the network. Experiments showed that the recognition accuracy with the proposed method was great higher than that with the BP neural network.

Outlier detection is an important data mining task for consistency checks, fraud detection, etc. Binary decision making on whether or not an object is an outlier is not appropriate in many applications and moreover hard to parametrize. Thus, recently, methods for outlier ranking have been proposed. Determining the degree of deviation, they do not require setting a decision boundary between outliers and the remaining data. High dimensional and heterogeneous (continuous and categorical attributes) data, however, pose a problem for most outlier ranking algorithms. In this work, we propose our OutRank approach for ranking outliers in heterogeneous high dimensional data. We introduce a consistent model for different attribute types. Our novel scoring functions transform the analyzed structure of the data to a meaningful ranking. Promising results in preliminary experiments show the potential for successful outlier ranking in high dimensional data.