An Effective Support Vector Data Description with Relevant Metric Learning
نویسندگان
چکیده
Support Vector Data Description (SVDD) as a one-class classifier was developed to construct the minimum hypersphere that encloses all the data of the target class in a high dimensional feature space. However, SVDD treats the features of all data equivalently in constructing the minimum hypersphere since it adopts Euclidean distance metric and lacks the incorporation of prior knowledge. In this paper, we propose an improved SVDD through introducing relevant metric learning. The presented method named RSVDD here assigns large weights to the relevant features and tights the similar data through incorporating the positive equivalence information in a natural way. In practice, we introduce relevant metric learning into the original SVDD model with the covariance matrices of the positive equivalence data. The experimental results on both synthetic and real data sets show that the proposed method can bring more accurate description for all the tested target cases than the conventional SVDD.
منابع مشابه
Data Dependent Distance Metric for Efficient Gaussian Processes Classification
The contributions of this work are threefold. First, various metric learning techniques are analyzed and systematically studied under a unified framework to highlight the criticality of data-dependent distance metric in machine learning. The metric learning algorithms are categorized as naive, semi-naive, complete and high-level metric learning, under a common distance measurement framework. Se...
متن کاملAn Effective Approach for Robust Metric Learning in the Presence of Label Noise
Many algorithms in machine learning, pattern recognition, and data mining are based on a similarity/distance measure. For example, the kNN classifier and clustering algorithms such as k-means require a similarity/distance function. Also, in Content-Based Information Retrieval (CBIR) systems, we need to rank the retrieved objects based on the similarity to the query. As generic measures such as ...
متن کاملBagging-like metric learning for support vector regression
Metric plays an important role in machine learning and pattern recognition. Though many available offthe-shelf metrics can be selected to achieve some learning tasks at hand such as for k-nearest neighbor classification and k-means clustering, such a selection is not necessarily always appropriate due to its independence on data itself. It has been proved that a task-dependent metric learned fr...
متن کاملMachine learning algorithms in air quality modeling
Modern studies in the field of environment science and engineering show that deterministic models struggle to capture the relationship between the concentration of atmospheric pollutants and their emission sources. The recent advances in statistical modeling based on machine learning approaches have emerged as solution to tackle these issues. It is a fact that, input variable type largely affec...
متن کاملComparison of classic regression methods with neural network and support vector machine in classifying groundwater resources
In the present era, classification of data is one of the most important issues in various sciences in order to detect and predict events. In statistics, the traditional view of these classifications will be based on classic methods and statistical models such as logistic regression. In the present era, known as the era of explosion of information, in most cases, we are faced with data that c...
متن کامل