Application Of Support Vector Dimensionality Reduction Machine For Multi-instance Learning

Unlike supervised learning,the data of multi-instance learning consist of "bags".Each bag contains several examples and each bags has an explicit label.The basic assumption of multi-instance learning is that the positive bag contains at least one positive instance(which one or ones are unknown),and the instance in negative bag are all negative instances.With the development of multi-instance learning applications,such as image recognition and text categorization,many algorithms based on multi-instance learning have been proposed.The dimensionality reduction algorithm of multi-instance learning has gradually been a research hotspot.The main research contents and innovations of this thesis are as follows:First of all,a method for obtaining projection matrix using support vector dimension reduction learning machine for multi-example learning problems is proposed.Firstly,each bags is initialized as a single eigenvector representation,so that the samples have clear labels.So the multi-instance learning can be extended to supervised learning.Then,the support vector machine is used for dimensionality reduction.The criterion for support vector machine classification is to maximize the interval of different types of samples,so the normal direction of the classification hyperplane is a good projection direction.However,different positive-single eigenvectors not only have a great influence on the model,but also affect the projection vector.How to optimize the single eigenvector and support vector machine model of the positive bags to obtain the optimal projection vector.In view of the difficulty of simultaneous optimization,this thesis uses the block coordinate ascent method to optimize alternately and iteratively.What's more,a method for obtaining projection matrix using momentum LI-norm linear discriminant analysis for multi-example learning problems is proposed.The general solution is similar to the first method.Firstly,the single feature vector of each bag is initialized as a sample,and the multi-instance learning is extended to the scope of supervised learning.Then,the alternate and iterative method is also used to optimize the two variables of the positive-single eigenvector and the projection matrix.Specifically,the more robust LI-norm linear discriminant analysis is used to solve the projection matrix.An existing method for solving LI-norm linear discriminant analysis is to use the gradient method based on custom gradient.The use of the momentum gradient method accelerates convergence and makes the results more accurate.Finally,the experiment proves the effectiveness of the two algorithms proposed in this thesis.