8,798 research outputs found
Discriminative Gaussian Process Latent Variable Model for Classification
Supervised learning is difficult with high dimensional input spacesand very small training sets, but accurate classification may bepossible if the data lie on a low-dimensional manifold. GaussianProcess Latent Variable Models can discover low dimensional manifoldsgiven only a small number of examples, but learn a latent spacewithout regard for class labels. Existing methods for discriminativemanifold learning (e.g., LDA, GDA) do constrain the class distributionin the latent space, but are generally deterministic and may notgeneralize well with limited training data. We introduce a method forGaussian Process Classification using latent variable models trainedwith discriminative priors over the latent space, which can learn adiscriminative latent space from a small training set
Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Feature learning forms the cornerstone for tackling challenging learning
problems in domains such as speech, computer vision and natural language
processing. In this paper, we consider a novel class of matrix and
tensor-valued features, which can be pre-trained using unlabeled samples. We
present efficient algorithms for extracting discriminative information, given
these pre-trained features and labeled samples for any related task. Our class
of features are based on higher-order score functions, which capture local
variations in the probability density function of the input. We establish a
theoretical framework to characterize the nature of discriminative information
that can be extracted from score-function features, when used in conjunction
with labeled samples. We employ efficient spectral decomposition algorithms (on
matrices and tensors) for extracting discriminative components. The advantage
of employing tensor-valued features is that we can extract richer
discriminative information in the form of an overcomplete representations.
Thus, we present a novel framework for employing generative models of the input
for discriminative learning.Comment: 29 page
Latent Fisher Discriminant Analysis
Linear Discriminant Analysis (LDA) is a well-known method for dimensionality
reduction and classification. Previous studies have also extended the
binary-class case into multi-classes. However, many applications, such as
object detection and keyframe extraction cannot provide consistent
instance-label pairs, while LDA requires labels on instance level for training.
Thus it cannot be directly applied for semi-supervised classification problem.
In this paper, we overcome this limitation and propose a latent variable Fisher
discriminant analysis model. We relax the instance-level labeling into
bag-level, is a kind of semi-supervised (video-level labels of event type are
required for semantic frame extraction) and incorporates a data-driven prior
over the latent variables. Hence, our method combines the latent variable
inference and dimension reduction in an unified bayesian framework. We test our
method on MUSK and Corel data sets and yield competitive results compared to
the baseline approach. We also demonstrate its capacity on the challenging
TRECVID MED11 dataset for semantic keyframe extraction and conduct a
human-factors ranking-based experimental evaluation, which clearly demonstrates
our proposed method consistently extracts more semantically meaningful
keyframes than challenging baselines.Comment: 12 page
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Unsupervised learning on imbalanced data is challenging because, when given
imbalanced data, current model is often dominated by the major category and
ignores the categories with small amount of data. We develop a latent variable
model that can cope with imbalanced data by dividing the latent space into a
shared space and a private space. Based on Gaussian Process Latent Variable
Models, we propose a new kernel formulation that enables the separation of
latent space and derives an efficient variational inference method. The
performance of our model is demonstrated with an imbalanced medical image
dataset.Comment: ICLR 2016 Worksho
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