10,173 research outputs found
Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
Due to its causal semantics, Bayesian networks (BN) have been widely employed
to discover the underlying data relationship in exploratory studies, such as
brain research. Despite its success in modeling the probability distribution of
variables, BN is naturally a generative model, which is not necessarily
discriminative. This may cause the ignorance of subtle but critical network
changes that are of investigation values across populations. In this paper, we
propose to improve the discriminative power of BN models for continuous
variables from two different perspectives. This brings two general
discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the
first framework, we employ Fisher kernel to bridge the generative models of GBN
and the discriminative classifiers of SVMs, and convert the GBN parameter
learning to Fisher kernel learning via minimizing a generalization error bound
of SVMs. In the second framework, we employ the max-margin criterion and build
it directly upon GBN models to explicitly optimize the classification
performance of the GBNs. The advantages and disadvantages of the two frameworks
are discussed and experimentally compared. Both of them demonstrate strong
power in learning discriminative parameters of GBNs for neuroimaging based
brain network analysis, as well as maintaining reasonable representation
capacity. The contributions of this paper also include a new Directed Acyclic
Graph (DAG) constraint with theoretical guarantee to ensure the graph validity
of GBN.Comment: 16 pages and 5 figures for the article (excluding appendix
Disturbance Grassmann Kernels for Subspace-Based Learning
In this paper, we focus on subspace-based learning problems, where data
elements are linear subspaces instead of vectors. To handle this kind of data,
Grassmann kernels were proposed to measure the space structure and used with
classifiers, e.g., Support Vector Machines (SVMs). However, the existing
discriminative algorithms mostly ignore the instability of subspaces, which
would cause the classifiers misled by disturbed instances. Thus we propose
considering all potential disturbance of subspaces in learning processes to
obtain more robust classifiers. Firstly, we derive the dual optimization of
linear classifiers with disturbance subject to a known distribution, resulting
in a new kernel, Disturbance Grassmann (DG) kernel. Secondly, we research into
two kinds of disturbance, relevant to the subspace matrix and singular values
of bases, with which we extend the Projection kernel on Grassmann manifolds to
two new kernels. Experiments on action data indicate that the proposed kernels
perform better compared to state-of-the-art subspace-based methods, even in a
worse environment.Comment: This paper include 3 figures, 10 pages, and has been accpeted to
SIGKDD'1
Supervised Dictionary Learning
It is now well established that sparse signal models are well suited to
restoration tasks and can effectively be learned from audio, image, and video
data. Recent research has been aimed at learning discriminative sparse models
instead of purely reconstructive ones. This paper proposes a new step in that
direction, with a novel sparse representation for signals belonging to
different classes in terms of a shared dictionary and multiple class-decision
functions. The linear variant of the proposed model admits a simple
probabilistic interpretation, while its most general variant admits an
interpretation in terms of kernels. An optimization framework for learning all
the components of the proposed model is presented, along with experimental
results on standard handwritten digit and texture classification tasks
Discriminative Features via Generalized Eigenvectors
Representing examples in a way that is compatible with the underlying
classifier can greatly enhance the performance of a learning system. In this
paper we investigate scalable techniques for inducing discriminative features
by taking advantage of simple second order structure in the data. We focus on
multiclass classification and show that features extracted from the generalized
eigenvectors of the class conditional second moments lead to classifiers with
excellent empirical performance. Moreover, these features have attractive
theoretical properties, such as inducing representations that are invariant to
linear transformations of the input. We evaluate classifiers built from these
features on three different tasks, obtaining state of the art results
A Review of Codebook Models in Patch-Based Visual Object Recognition
The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods
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