Pattern classification is a major problem in machine learning research[32, 5, 6, 13]. The two most important topics of pattern classification are data representation and classifier learning. Zhang et al. proposed an efficient multi-model classifier for large scale Bio-sequence localization prediction . Zhang et al. developed and optimized association rule mining algorithms and implemented them on paralleled micro-architectural platforms [39, 38]. Most data representation and classification methods are based on single data point. When one data point is considered for representation and classification, all other data points are ignored. However, the other data points other than the data point under consideration, which are called contextual data points, may play important roles in its representation and classification. It is necessary to explore the contexts of data points when they are represented and/or classified. In this paper, we investigate the problem of learning effective representation of a data point from its context guided by its class label, and proposed a novel supervised context learning method using sparse regularization and linear classifier learning formulation.
We propose a novel method to explore the context of a data point, and use it to represent it. We use its nearest neighbors as its context, and try to reconstruct it by the data points in its context. The reconstruction errors are imposed to be spares. Moreover, the reconstruction result is used as the new representation of this data point. We apply a linear function to predict its class label from the sparse reconstruction of its context. The motivation of this contribution is that for each data point, only a few data points in its context is of the same class as itself. To find the critical contextual data points, we proposed to learn the classifier together with she sparse context. We mode this problem as a minimization problem. In this problem, the context reconstruction error, reconstruction sparsity, classification error, and classifier complexity are minimized simultaneously. We also problem a novel iterative algorithm to solve this minimization problem. We first reformulate it as ist Lagrange formula, and the use an alterative optimization method to solve it.
2 Proposed method
We consider a binary classification problem, and a training set of data points are given as , where is a
-dimensional feature vector of the-th data point, and is the class label of the -th point. To learn from the context of the -th data point, we find its nearest neighbors and denote them as , where is the -th nearest neighbor of the -th point. They are further organized as a matrix , where the -th column is . We represent by linearly reconstructing it from its contextual points as
where is its reconstruction, and is the reconstruction coefficient of the -th nearest neighbor. is the reconstruction coefficient vector of the -th data point. The reconstruction coefficient vectors of all the training points are organized in reconstruction coefficient matrix , with its -th column as . To solve the reconstruction coefficient vectors, we propose the following minimization problem,
where and are trade-off parameters. In the objective of this problem, the first term is to minimize the reconstruction error measured by a squared norm penalty between and , and the second term is a norm penalty to the contextual reconstruction coefficient vector .
We design a classifier to classify the -th data point,
where is the classifier parameter vector. The following optimization problem is proposed to learn w,
where is the the squared norm regularization term to reduce the complexity of the classifier, is the slack variable for the hinge loss of the -th training point, and is a tradeoff parameter.
According to the dual theory of optimization, the following dual optimization problem is obtained,
where , and are Lagrange multipliers. By setting the partial derivative of with regard to w and to zeros, we have
where is a dimensional vector of all elements. We solve this problem with the alternate optimization strategy. In each iteration of an iterative algorithm, we fix first to solve , and then fix to solve .
When is fixed and only is considered, we solve one by one, (8) is further reduced to
This problem could be solved efficiently by the modified feature-sign search algorithm proposed by Gao et al. .
When is fixed and only is considered, the problem in (8) is reduced to
This problem is a typical constrained quadratic programming (QP) problem, and it can be solved efficiently by the active set algorithm.
In this section, we evaluate the proposed supervised sparse context learning (SSCL) algorithm on several benchmark data sets.
3.1 Experiment setup
In the experiments, we used three date sets, which are introduced as follows:
MANET loss data set: The packet losses of the receiver in mobile Ad hoc networks (MANET) can be classified into three types, which are wireless random errors caused losses, the route change losses induced by node mobility and network congestion. We collect 381 data points for the congestion loss, 458 for the route change loss, and 516 data points for the wireless error loss for this data set. Thus in the data set, there are 1355 data points in total. To extract the feature vector each data point, we calculate 12 features from each data point as in , and concatenate them to form a vector.
Twitter data set: The second data set is a Twitter data set. The target of this data set is to predict the gender of the twitter user, male or female, given one of his/her Twitter massage. We collected 53,971 twitter massages in total, and among them there are 28,012 messages sent by male users, and 25,959 messages sent by female users. To extract features from each Twitter message, we extract Term features, linguistic features, and medium diversity features as gender-specific features as in .
Arrhythmia data set: The third data set is publicly available at http://arc
hive.ics.uci.edu/ml/datasets/Arrhythmia. In this data set, there are 452 data points, and they belongs to 16 different classes. Each data point has a feature vector of 279 features.
To conduct the experiments, we used the 10-fold cross validation.
3.2 Experimental Results
Since the proposed algorithm is a context-based classification and sparse representation method, we compared the proposed algorithm to three popular context-based classifiers, and one context-based sparse representation method. The three context-based classifiers are traditional
-nearest neighbor classifier (KNN), sparse representation based classification (SRBC)
,and Laplacian support vector machine (LSVM). The context-based sparse representation method is Gao et al.’s Laplacian sparse coding (LSC) . The boxplots of the 10-fold cross validation of the compared algorithms are given in figure 1. From the figures, we can see that the proposed method SSCL outperforms all the other methods on all three data sets. The second best method is SRBC, which also uses sparse context to represent the data point. This is a strong evidence that learning a supervised sparse context is critical for classification problem.
3.2.1 Sensitivity to parameters
In the proposed formulation, there are three tradeoff parameters, , , and . We plot the curve of mean prediction accuracies against different values of parameters, and show them in figure 2. From figure 2(a) and 2(b), we can see the accuracy is stable to the parameter and . From figure 2(c), we can see a larger leads to better classification performances.
4 Conclusion and future works
In this paper, we study the problem of using context to represent and classify data points. We propose to use a sparse linear combination of the data points in the context of a data point to represent itself. Moreover, to increase the discriminative ability of the new representation, we develop an supervised method to learn the sparse context by learning it and a classifier together in an unified optimization framework. Experiments on three benchmark data sets show its advantage over state-of-the-art context-based data representation and classification methods. In the future, we will extend the proposed method to applications of information security [33, 27, 30, 29, 28, 31, 34], bioinformatics [25, 24, 23, 12, 15, 14, 7, 37, 7]16, 17], and big data analysis using high performance computing [43, 18, 9, 35, 4, 41, 40, 39, 38, 35, 10, 42, 21, 20, 43, 19, 22].
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