5 research outputs found
Multiple kernel multivariate performance learning using cutting plane algorithm
In this paper, we propose a multi-kernel classifier learning algorithm to
optimize a given nonlinear and nonsmoonth multivariate classifier performance
measure. Moreover, to solve the problem of kernel function selection and kernel
parameter tuning, we proposed to construct an optimal kernel by weighted linear
combination of some candidate kernels. The learning of the classifier parameter
and the kernel weight are unified in a single objective function considering to
minimize the upper boundary of the given multivariate performance measure. The
objective function is optimized with regard to classifier parameter and kernel
weight alternately in an iterative algorithm by using cutting plane algorithm.
The developed algorithm is evaluated on two different pattern classification
methods with regard to various multivariate performance measure optimization
problems. The experiment results show the proposed algorithm outperforms the
competing methods
Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier
Multiview learning problem refers to the problem of learning a classifier
from multiple view data. In this data set, each data points is presented by
multiple different views. In this paper, we propose a novel method for this
problem. This method is based on two assumptions. The first assumption is that
each data point has an intact feature vector, and each view is obtained by a
linear transformation from the intact vector. The second assumption is that the
intact vectors are discriminative, and in the intact space, we have a linear
classifier to separate the positive class from the negative class. We define an
intact vector for each data point, and a view-conditional transformation matrix
for each view, and propose to reconstruct the multiple view feature vectors by
the product of the corresponding intact vectors and transformation matrices.
Moreover, we also propose a linear classifier in the intact space, and learn it
jointly with the intact vectors. The learning problem is modeled by a
minimization problem, and the objective function is composed of a Cauchy error
estimator-based view-conditional reconstruction term over all data points and
views, and a classification error term measured by hinge loss over all the
intact vectors of all the data points. Some regularization terms are also
imposed to different variables in the objective function. The minimization
problem is solve by an iterative algorithm using alternate optimization
strategy and gradient descent algorithm. The proposed algorithm shows it
advantage in the compression to other multiview learning algorithms on
benchmark data sets
Manifold regularization in structured output space for semi-supervised structured output prediction
Structured output prediction aims to learn a predictor to predict a
structured output from a input data vector. The structured outputs include
vector, tree, sequence, etc. We usually assume that we have a training set of
input-output pairs to train the predictor. However, in many real-world appli-
cations, it is difficult to obtain the output for a input, thus for many
training input data points, the structured outputs are missing. In this paper,
we dis- cuss how to learn from a training set composed of some input-output
pairs, and some input data points without outputs. This problem is called semi-
supervised structured output prediction. We propose a novel method for this
problem by constructing a nearest neighbor graph from the input space to
present the manifold structure, and using it to regularize the structured out-
put space directly. We define a slack structured output for each training data
point, and proposed to predict it by learning a structured output predictor.
The learning of both slack structured outputs and the predictor are unified
within one single minimization problem. In this problem, we propose to mini-
mize the structured loss between the slack structured outputs of neighboring
data points, and the prediction error measured by the structured loss. The
problem is optimized by an iterative algorithm. Experiment results over three
benchmark data sets show its advantage
A novel multivariate performance optimization method based on sparse coding and hyper-predictor learning
In this paper, we investigate the problem of optimization multivariate
performance measures, and propose a novel algorithm for it. Different from
traditional machine learning methods which optimize simple loss functions to
learn prediction function, the problem studied in this paper is how to learn
effective hyper-predictor for a tuple of data points, so that a complex loss
function corresponding to a multivariate performance measure can be minimized.
We propose to present the tuple of data points to a tuple of sparse codes via a
dictionary, and then apply a linear function to compare a sparse code against a
give candidate class label. To learn the dictionary, sparse codes, and
parameter of the linear function, we propose a joint optimization problem. In
this problem, the both the reconstruction error and sparsity of sparse code,
and the upper bound of the complex loss function are minimized. Moreover, the
upper bound of the loss function is approximated by the sparse codes and the
linear function parameter. To optimize this problem, we develop an iterative
algorithm based on descent gradient methods to learn the sparse codes and
hyper-predictor parameter alternately. Experiment results on some benchmark
data sets show the advantage of the proposed methods over other
state-of-the-art algorithms
Domain Transfer Multi-Instance Dictionary Learning
In this paper, we invest the domain transfer learning problem with
multi-instance data. We assume we already have a well-trained multi-instance
dictionary and its corresponding classifier from the source domain, which can
be used to represent and classify the bags. But it cannot be directly used to
the target domain. Thus we propose to adapt them to the target domain by adding
an adaptive term to the source domain classifier. The adaptive function is a
linear function based a domain transfer multi-instance dictionary. Given a
target domain bag, we first map it to a bag-level feature space using the
domain transfer dictionary, and then apply a the linear adaptive function to
its bag-level feature vector. To learn the domain-transfer dictionary and the
adaptive function parameter, we simultaneously minimize the average
classification error of the target domain classifier over the target domain
training set, and the complexities of both the adaptive function parameter and
the domain transfer dictionary. The minimization problem is solved by an
iterative algorithm which update the dictionary and the function parameter
alternately. Experiments over several benchmark data sets show the advantage of
the proposed method over existing state-of-the-art domain transfer
multi-instance learning methods