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About 1,046 papers for “Random subspace method”

Random Subspace Method in Text Categorization

Mehrdad J. Gangeh, Mohamed S. Kamel, Robert P. W. Duin · 2010

In text categorization (TC), which is a supervised technique, a feature vector of terms or phrases is usually used to represent the documents. Due to the huge number of terms in even a moderate-size text corpus, high dimensional feature space is an intrinsic problem in TC. Random subspace method (RSM), a technique that divides the feature space to smaller ones each submitted to a (base) classifier (BC) in an ensemble, can be an effective approach to reduce the dimensionality of the feature space. Inspired by a similar research on functional magnetic resonance imaging (fMRI) of brain, here we address the estimation of ensemble parameters, i.e., the ensemble size (L) and the dimensionality of feature subsets (M) by defining three criteria: usability, coverage, and diversity of the ensemble. We will show that relatively medium M and small L yield an ensemble that improves the performance of a single support vector machine, which is considered as the state-of-the-art in TC.

Cited by 16

Random Subspace Method for Gait Recognition

Yu Guan, Chang‐Tsun Li, Yongjian Hu · 2012

Over fitting is a common problem for gait recognition algorithms when gait sequences in gallery for training are acquired under a single walking condition. In this paper, we propose an approach based on the random subspace method (RSM) to address such over learning problems. Initially, two-dimensional Principle Component Analysis (2DPCA) is adopted to obtain the full hypothesis space (i.e., eigen space). Multiple inductive biases (i.e., subspaces) are constructed, each with the corresponding basis vectors randomly chosen from the initial eigen space. This procedure can not only largely avoid over adaptation but also facilitate dimension reduction. The final classification is achieved by the decision committee which follows a majority voting criterion from the labeling results of all the subspaces. Experimental results on the benchmark USF Human ID gait database show that the proposed method is a feasible framework for gait recognition under unknown walking conditions.

Cited by 20

Random Rule Sets – Combining Random Covering with the Random Subspace Method

Tony Lindgren · International Journal of Machine Learning and Computing · 2018

Ensembles of classifiers have been proven to be among the best methods to create highly accurate prediction models. In this study, we combine the random coverage method, which facilitates additional diversity when inducing rules using the covering algorithm, with the random subspace selection method, which has been used successfully by the random forest algorithm. We compare three different covering methods with the random forest algorithm: (1) using random subspace selection and random coverage, (2) using bagging and random subspace selection, and (3) using bagging, random subspace selection, and random coverage. The results show that all three covering algorithms perform better than the random forest algorithm. The covering algorithm using random subspace selection and random coverage performs best among all methods. The results are not significant according to adjusted p values but they are according to unadjusted p values, indicating that the novel method introduced in this study warrants further attention.

Cited by 2Open access

A random subspace method for co-training

Jiao Wang, Luo Siwei, Xianhua Zeng · 2008

Semi-supervised learning has received much attention recently. Co-training is a kind of semi-supervised learning method which uses unlabeled data to improve the performance of standard supervised learning algorithms. A novel co-training style algorithm, RASCO (for RAndom Subspace CO-training), is proposed which uses stochastic discrimination theory to extend co-training to multi-view situation. The accuracy and generalizability of RASCO are analyzed. The influences of the parameters of RASCO are discussed. Experiments on UCI data set demonstrate that RASCO is more effective than other co-training style algorithms.

Cited by 62

Random Subspace Method for One-Class Classifiers

Veronika Cheplygina · Research Repository (Delft University of Technology) · 2010

The goal of one-class classi cation is to distinguish the target class from all the other classes using only training data of the target class. It may be di cult for a single one-class classi er to capture all the characteristics of the target class, which may result in misclassi cations of new objects. In order to create more accurate classi ers, combining several one-class classi ers is possible. Previous research has shown that the Random Subspace Method, in which classi ers are trained on different subsets of the feature space, can be e ective for one-class classi ers. However, there are indications that it is not straightforward to achieve improved performance with this method. In ensembles of traditional classi ers, it has been shown that pruning inaccurate classi ers can be more e ective than using all the classi ers. We propose to apply pruning to RSM of one-class classi ers. Results from the performed experiments show the that pruned ensemble has better and more stable performance than the complete ensemble. A comparison of the proposed ensemble with other ensemble techniques also indicates very competitive performance.

Cited by 1Open access

A semi-random subspace method for classification ensembles

Mehmet Fatih Amasyalı · 2013

The performance of ensemble algorithms is related with two terms: the individual accuracy of base learners and the diversity of their results. Random Subspace algorithm owes its success to the diversity. In this study, we propose a method (Semi Random Subspace) which increases its diversity. We compare our method and original Random Subspace over 36 datasets. The experiments show that our method is superior to the original Random Subspace. But its advantage is limited with the size of the ensemble. In this situation, we can say that Semi Random Subspace is suitable choice for the small ensembles.

Cited by 1

SVM-based relevance feedback using random subspace method

Dacheng Tao, Xiaoou Tang · 2005

Relevance feedback (RF) schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM based RF is often poor when the number of labeled feedback samples is small. In order to solve this problem, we propose an RF algorithm using the random subspace method. The algorithm can overcome the classifier unstable and over-fitting problems that are common to the SVM based RF. Through extensive experiments on 17, 800 images, the proposed algorithm is shown to outperform existing algorithms significantly.

Cited by 6

The random subspace method for constructing decision forests

Tin Kam Ho · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1998

Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces. The subspace method is compared to single-tree classifiers and other forest construction methods by experiments on publicly available datasets, where the method's superiority is demonstrated. We also discuss independence between trees in a forest and relate that to the combined classification accuracy.

Cited by 6,737

regRSM: Random Subspace Method (RSM) for Linear Regression

Pawel Teisseyre, Robert A. Klopotek · 2014

Performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.

Cited by 0Open access

Weighted random subspace method for high dimensional data classification

Xiaoye Li, Hongyu Zhao · Statistics and Its Interface · 2009

High dimensional data, especially those emerging from genomics and proteomics studies, pose significant challenges to traditional classification algorithms because the performance of these algorithms may substantially deteriorate due to high dimensionality and existence of many noisy features in these data. To address these problems, pre-classification feature selection and aggregating algorithms have been proposed. However, most feature selection procedures either fail to consider potential interactions among the features or tend to over fit the data. The aggregating algorithms, e.g. the bagging predictor, the boosting algorithm, the random subspace method, and the Random Forests algorithm, are promising in handling high dimensional data. However, there is a lack of attention to optimal weight assignments to individual classifiers and this has prevented these algorithms from achieving better classification accuracy. In this article, we formulate the weight assignment problem and propose a heuristic optimization solution.We have applied the proposed weight assignment procedures to the random subspace method to develop a weighted random subspace method. Several public gene expression and mass spectrometry data sets at the Kent Ridge biomedical data repository have been analyzed by this novel method. We have found that significant improvement over the common equal weight assignment scheme may be achieved by our method.

Cited by 29Open access

An improved subspace weighting method using random matrix theory

Yumeng Gao, Jianghui Li, Yechao Bai et al. · Frontiers of Information Technology & Electronic Engineering · 2020

The weighting subspace fitting (WSF) algorithm performs better than the multi-signal classification (MUSIC) algorithm in the case of low signal-to-noise ratio (SNR) and when signals are correlated. In this study, we use the random matrix theory (RMT) to improve WSF. RMT focuses on the asymptotic behavior of eigenvalues and eigenvectors of random matrices with dimensions of matrices increasing at the same rate. The approximative first-order perturbation is applied in WSF when calculating statistics of the eigenvectors of sample covariance. Using the asymptotic results of the norm of the projection from the sample covariance matrix signal subspace onto the real signal in the random matrix theory, the method of calculating WSF is obtained. Numerical results are shown to prove the superiority of RMT in scenarios with few snapshots and a low SNR.

Cited by 1