493 research outputs found
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models
Abnormal event detection is one of the important objectives in research and
practical applications of video surveillance. However, there are still three
challenging problems for most anomaly detection systems in practical setting:
limited labeled data, ambiguous definition of "abnormal" and expensive feature
engineering steps. This paper introduces a unified detection framework to
handle these challenges using energy-based models, which are powerful tools for
unsupervised representation learning. Our proposed models are firstly trained
on unlabeled raw pixels of image frames from an input video rather than
hand-crafted visual features; and then identify the locations of abnormal
objects based on the errors between the input video and its reconstruction
produced by the models. To handle video stream, we develop an online version of
our framework, wherein the model parameters are updated incrementally with the
image frames arriving on the fly. Our experiments show that our detectors,
using Restricted Boltzmann Machines (RBMs) and Deep Boltzmann Machines (DBMs)
as core modules, achieve superior anomaly detection performance to unsupervised
baselines and obtain accuracy comparable with the state-of-the-art approaches
when evaluating at the pixel-level. More importantly, we discover that our
system trained with DBMs is able to simultaneously perform scene clustering and
scene reconstruction. This capacity not only distinguishes our method from
other existing detectors but also offers a unique tool to investigate and
understand how the model works.Comment: This manuscript is under consideration at Pattern Recognition Letter
A Random Finite Set Model for Data Clustering
The goal of data clustering is to partition data points into groups to
minimize a given objective function. While most existing clustering algorithms
treat each data point as vector, in many applications each datum is not a
vector but a point pattern or a set of points. Moreover, many existing
clustering methods require the user to specify the number of clusters, which is
not available in advance. This paper proposes a new class of models for data
clustering that addresses set-valued data as well as unknown number of
clusters, using a Dirichlet Process mixture of Poisson random finite sets. We
also develop an efficient Markov Chain Monte Carlo posterior inference
technique that can learn the number of clusters and mixture parameters
automatically from the data. Numerical studies are presented to demonstrate the
salient features of this new model, in particular its capacity to discover
extremely unbalanced clusters in data.Comment: In Proceedings of International Conference on Fusion (FUSION),
Salamanca, Spain, July 201
Nonnegative Restricted Boltzmann Machines for Parts-based Representations Discovery and Predictive Model Stabilization
The success of any machine learning system depends critically on effective
representations of data. In many cases, it is desirable that a representation
scheme uncovers the parts-based, additive nature of the data. Of current
representation learning schemes, restricted Boltzmann machines (RBMs) have
proved to be highly effective in unsupervised settings. However, when it comes
to parts-based discovery, RBMs do not usually produce satisfactory results. We
enhance such capacity of RBMs by introducing nonnegativity into the model
weights, resulting in a variant called nonnegative restricted Boltzmann machine
(NRBM). The NRBM produces not only controllable decomposition of data into
interpretable parts but also offers a way to estimate the intrinsic nonlinear
dimensionality of data, and helps to stabilize linear predictive models. We
demonstrate the capacity of our model on applications such as handwritten digit
recognition, face recognition, document classification and patient readmission
prognosis. The decomposition quality on images is comparable with or better
than what produced by the nonnegative matrix factorization (NMF), and the
thematic features uncovered from text are qualitatively interpretable in a
similar manner to that of the latent Dirichlet allocation (LDA). The stability
performance of feature selection on medical data is better than RBM and
competitive with NMF. The learned features, when used for classification, are
more discriminative than those discovered by both NMF and LDA and comparable
with those by RBM
Multi-Generator Generative Adversarial Nets
We propose a new approach to train the Generative Adversarial Nets (GANs)
with a mixture of generators to overcome the mode collapsing problem. The main
intuition is to employ multiple generators, instead of using a single one as in
the original GAN. The idea is simple, yet proven to be extremely effective at
covering diverse data modes, easily overcoming the mode collapse and delivering
state-of-the-art results. A minimax formulation is able to establish among a
classifier, a discriminator, and a set of generators in a similar spirit with
GAN. Generators create samples that are intended to come from the same
distribution as the training data, whilst the discriminator determines whether
samples are true data or generated by generators, and the classifier specifies
which generator a sample comes from. The distinguishing feature is that
internal samples are created from multiple generators, and then one of them
will be randomly selected as final output similar to the mechanism of a
probabilistic mixture model. We term our method Mixture GAN (MGAN). We develop
theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon
divergence (JSD) between the mixture of generators' distributions and the
empirical data distribution is minimal, whilst the JSD among generators'
distributions is maximal, hence effectively avoiding the mode collapse. By
utilizing parameter sharing, our proposed model adds minimal computational cost
to the standard GAN, and thus can also efficiently scale to large-scale
datasets. We conduct extensive experiments on synthetic 2D data and natural
image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior
performance of our MGAN in achieving state-of-the-art Inception scores over
latest baselines, generating diverse and appealing recognizable objects at
different resolutions, and specializing in capturing different types of objects
by generators
Analogical-based Bayesian Optimization
Some real-world problems revolve to solve the optimization problem
\max_{x\in\mathcal{X}}f\left(x\right) where f\left(.\right) is a black-box
function and X might be the set of non-vectorial objects (e.g., distributions)
where we can only define a symmetric and non-negative similarity score on it.
This setting requires a novel view for the standard framework of Bayesian
Optimization that generalizes the core insightful spirit of this framework.
With this spirit, in this paper, we propose Analogical-based Bayesian
Optimization that can maximize black-box function over a domain where only a
similarity score can be defined. Our pathway is as follows: we first base on
the geometric view of Gaussian Processes (GP) to define the concept of
influence level that allows us to analytically represent predictive means and
variances of GP posteriors and base on that view to enable replacing kernel
similarity by a more genetic similarity score. Furthermore, we also propose two
strategies to find a batch of query points that can efficiently handle high
dimensional data
Dual Discriminator Generative Adversarial Nets
We propose in this paper a novel approach to tackle the problem of mode
collapse encountered in generative adversarial network (GAN). Our idea is
intuitive but proven to be very effective, especially in addressing some key
limitations of GAN. In essence, it combines the Kullback-Leibler (KL) and
reverse KL divergences into a unified objective function, thus it exploits the
complementary statistical properties from these divergences to effectively
diversify the estimated density in capturing multi-modes. We term our method
dual discriminator generative adversarial nets (D2GAN) which, unlike GAN, has
two discriminators; and together with a generator, it also has the analogy of a
minimax game, wherein a discriminator rewards high scores for samples from data
distribution whilst another discriminator, conversely, favoring data from the
generator, and the generator produces data to fool both two discriminators. We
develop theoretical analysis to show that, given the maximal discriminators,
optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL
divergences between data distribution and the distribution induced from the
data generated by the generator, hence effectively avoiding the mode collapsing
problem. We conduct extensive experiments on synthetic and real-world
large-scale datasets (MNIST, CIFAR-10, STL-10, ImageNet), where we have made
our best effort to compare our D2GAN with the latest state-of-the-art GAN's
variants in comprehensive qualitative and quantitative evaluations. The
experimental results demonstrate the competitive and superior performance of
our approach in generating good quality and diverse samples over baselines, and
the capability of our method to scale up to ImageNet database
Collaborative filtering via sparse Markov random fields
Recommender systems play a central role in providing individualized access to
information and services. This paper focuses on collaborative filtering, an
approach that exploits the shared structure among mind-liked users and similar
items. In particular, we focus on a formal probabilistic framework known as
Markov random fields (MRF). We address the open problem of structure learning
and introduce a sparsity-inducing algorithm to automatically estimate the
interaction structures between users and between items. Item-item and user-user
correlation networks are obtained as a by-product. Large-scale experiments on
movie recommendation and date matching datasets demonstrate the power of the
proposed method
Mixed-Variate Restricted Boltzmann Machines
Modern datasets are becoming heterogeneous. To this end, we present in this
paper Mixed-Variate Restricted Boltzmann Machines for simultaneously modelling
variables of multiple types and modalities, including binary and continuous
responses, categorical options, multicategorical choices, ordinal assessment
and category-ranked preferences. Dependency among variables is modeled using
latent binary variables, each of which can be interpreted as a particular
hidden aspect of the data. The proposed model, similar to the standard RBMs,
allows fast evaluation of the posterior for the latent variables. Hence, it is
naturally suitable for many common tasks including, but not limited to, (a) as
a pre-processing step to convert complex input data into a more convenient
vectorial representation through the latent posteriors, thereby offering a
dimensionality reduction capacity, (b) as a classifier supporting binary,
multiclass, multilabel, and label-ranking outputs, or a regression tool for
continuous outputs and (c) as a data completion tool for multimodal and
heterogeneous data. We evaluate the proposed model on a large-scale dataset
using the world opinion survey results on three tasks: feature extraction and
visualization, data completion and prediction.Comment: Originally published in Proceedings of ACML'1
Learning Structured Outputs from Partial Labels using Forest Ensemble
Learning structured outputs with general structures is computationally
challenging, except for tree-structured models. Thus we propose an efficient
boosting-based algorithm AdaBoost.MRF for this task. The idea is based on the
realization that a graph is a superimposition of trees. Different from most
existing work, our algorithm can handle partial labelling, and thus is
particularly attractive in practice where reliable labels are often sparsely
observed. In addition, our method works exclusively on trees and thus is
guaranteed to converge. We apply the AdaBoost.MRF algorithm to an indoor video
surveillance scenario, where activities are modelled at multiple levels.Comment: Conference version appeared in Truyen et al, AdaBoost.MRF: Boosted
Markov random forests and application to multilevel activity recognition.
CVPR'0
Learning From Ordered Sets and Applications in Collaborative Ranking
Ranking over sets arise when users choose between groups of items. For
example, a group may be of those movies deemed stars to them, or a
customized tour package. It turns out, to model this data type properly, we
need to investigate the general combinatorics problem of partitioning a set and
ordering the subsets. Here we construct a probabilistic log-linear model over a
set of ordered subsets. Inference in this combinatorial space is highly
challenging: The space size approaches as approaches
infinity. We propose a \texttt{split-and-merge} Metropolis-Hastings procedure
that can explore the state-space efficiently. For discovering hidden aspects in
the data, we enrich the model with latent binary variables so that the
posteriors can be efficiently evaluated. Finally, we evaluate the proposed
model on large-scale collaborative filtering tasks and demonstrate that it is
competitive against state-of-the-art methods.Comment: JMLR: Workshop and Conference Proceedings 25:1-16, 2012, Asian
Conference on Machine Learnin
- …