46,278 research outputs found
Data-Adaptive Kernel Support Vector Machine
In this thesis, we propose the data-adaptive kernel Support Vector Machine (SVM), a new method with a data-driven scaling kernel function based on real data sets. This two-stage approach of kernel function scaling can enhance the accuracy of a support vector machine, especially when the data are imbalanced. Followed by the standard SVM procedure in the first stage, the proposed method locally adapts the kernel function to data locations based on the skewness of the class outcomes. In the second stage, the decision rule is constructed with the data-adaptive kernel function and is used as the classifier. This process enlarges the magnification effect directly on the Riemannian manifold within the feature space rather than the input space. The proposed data-adaptive kernel SVM technique is applied in the binary classification, and is extended to the multi-class situations when imbalance is a main concern. We conduct extensive simulation studies to assess the performance of the proposed methods, and the prostate cancer image study is employed as an illustration. The data-adaptive kernel is further applied in feature selection process. We propose the data-adaptive kernel-penalized SVM, a new method of simultaneous feature selection and classification by penalizing data-adaptive kernels in SVMs. Instead of penalizing the standard cost function of SVMs in the usual way, the penalty will be directly added to the dual objective function that contains the data-adaptive kernel. Classification results with sparse features selected can be obtained simultaneously. Different penalty terms in the data-adaptive kernel-penalized SVM will be compared. The oracle property of the estimator is examined. We conduct extensive simulation studies to assess the performance of all the proposed methods, and employ the method on a breast cancer data set as an illustration.
The data-adaptive kernel is further applied in feature selection process. We propose the data-adaptive kernel-penalized SVM, a new method of simultaneous feature selection and classification by penalizing data-adaptive kernels in SVMs. Instead of penalizing the standard cost function of SVMs in the usual way, the penalty will be directly added to the dual objective function that contains the data-adaptive kernel. Classification results with sparse features selected can be obtained simultaneously. Different penalty terms in the data-adaptive kernel-penalized SVM will be compared. The oracle property of the estimator is examined. We conduct extensive simulation studies to assess the performance of all the proposed methods, and employ the method on a breast cancer data set as an illustration
Variable selection for the multicategory SVM via adaptive sup-norm regularization
The Support Vector Machine (SVM) is a popular classification paradigm in
machine learning and has achieved great success in real applications. However,
the standard SVM can not select variables automatically and therefore its
solution typically utilizes all the input variables without discrimination.
This makes it difficult to identify important predictor variables, which is
often one of the primary goals in data analysis. In this paper, we propose two
novel types of regularization in the context of the multicategory SVM (MSVM)
for simultaneous classification and variable selection. The MSVM generally
requires estimation of multiple discriminating functions and applies the argmax
rule for prediction. For each individual variable, we propose to characterize
its importance by the supnorm of its coefficient vector associated with
different functions, and then minimize the MSVM hinge loss function subject to
a penalty on the sum of supnorms. To further improve the supnorm penalty, we
propose the adaptive regularization, which allows different weights imposed on
different variables according to their relative importance. Both types of
regularization automate variable selection in the process of building
classifiers, and lead to sparse multi-classifiers with enhanced
interpretability and improved accuracy, especially for high dimensional low
sample size data. One big advantage of the supnorm penalty is its easy
implementation via standard linear programming. Several simulated examples and
one real gene data analysis demonstrate the outstanding performance of the
adaptive supnorm penalty in various data settings.Comment: Published in at http://dx.doi.org/10.1214/08-EJS122 the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Sparse multinomial kernel discriminant analysis (sMKDA)
Dimensionality reduction via canonical variate analysis (CVA) is important for pattern recognition and has been extended variously to permit more flexibility, e.g. by "kernelizing" the formulation. This can lead to over-fitting, usually ameliorated by regularization. Here, a method for sparse, multinomial kernel discriminant analysis (sMKDA) is proposed, using a sparse basis to control complexity. It is based on the connection between CVA and least-squares, and uses forward selection via orthogonal least-squares to approximate a basis, generalizing a similar approach for binomial problems. Classification can be performed directly via minimum Mahalanobis distance in the canonical variates. sMKDA achieves state-of-the-art performance in terms of accuracy and sparseness on 11 benchmark datasets
Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository
Machine learning qualifies computers to assimilate with data, without being
solely programmed [1, 2]. Machine learning can be classified as supervised and
unsupervised learning. In supervised learning, computers learn an objective
that portrays an input to an output hinged on training input-output pairs [3].
Most efficient and widely used supervised learning algorithms are K-Nearest
Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor
(LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this
paper is to implement these elegant learning algorithms on eleven different
datasets from the UCI machine learning repository to observe the variation of
accuracies for each of the algorithms on all datasets. Analyzing the accuracy
of the algorithms will give us a brief idea about the relationship of the
machine learning algorithms and the data dimensionality. All the algorithms are
developed in Matlab. Upon such accuracy observation, the comparison can be
built among KNN, SVM, LMNN, and ENN regarding their performances on each
dataset.Comment: To be published in the 4th IEEE International Conference on
Electrical Engineering and Information & Communication Technology (iCEEiCT
2018
Convex Learning of Multiple Tasks and their Structure
Reducing the amount of human supervision is a key problem in machine learning
and a natural approach is that of exploiting the relations (structure) among
different tasks. This is the idea at the core of multi-task learning. In this
context a fundamental question is how to incorporate the tasks structure in the
learning problem.We tackle this question by studying a general computational
framework that allows to encode a-priori knowledge of the tasks structure in
the form of a convex penalty; in this setting a variety of previously proposed
methods can be recovered as special cases, including linear and non-linear
approaches. Within this framework, we show that tasks and their structure can
be efficiently learned considering a convex optimization problem that can be
approached by means of block coordinate methods such as alternating
minimization and for which we prove convergence to the global minimum.Comment: 26 pages, 1 figure, 2 table
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