32 research outputs found
Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination
In this paper, we consider multi-sensor classification when there is a large
number of unlabeled samples. The problem is formulated under the multi-view
learning framework and a Consensus-based Multi-View Maximum Entropy
Discrimination (CMV-MED) algorithm is proposed. By iteratively maximizing the
stochastic agreement between multiple classifiers on the unlabeled dataset, the
algorithm simultaneously learns multiple high accuracy classifiers. We
demonstrate that our proposed method can yield improved performance over
previous multi-view learning approaches by comparing performance on three real
multi-sensor data sets.Comment: 5 pages, 4 figures, Accepted in 40th IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 15
Multi-view Regularized Gaussian Processes
Gaussian processes (GPs) have been proven to be powerful tools in various
areas of machine learning. However, there are very few applications of GPs in
the scenario of multi-view learning. In this paper, we present a new GP model
for multi-view learning. Unlike existing methods, it combines multiple views by
regularizing marginal likelihood with the consistency among the posterior
distributions of latent functions from different views. Moreover, we give a
general point selection scheme for multi-view learning and improve the proposed
model by this criterion. Experimental results on multiple real world data sets
have verified the effectiveness of the proposed model and witnessed the
performance improvement through employing this novel point selection scheme
Manifold Relevance Determination
In this paper we present a fully Bayesian latent variable model which
exploits conditional nonlinear(in)-dependence structures to learn an efficient
latent representation. The latent space is factorized to represent shared and
private information from multiple views of the data. In contrast to previous
approaches, we introduce a relaxation to the discrete segmentation and allow
for a "softly" shared latent space. Further, Bayesian techniques allow us to
automatically estimate the dimensionality of the latent spaces. The model is
capable of capturing structure underlying extremely high dimensional spaces.
This is illustrated by modelling unprocessed images with tenths of thousands of
pixels. This also allows us to directly generate novel images from the trained
model by sampling from the discovered latent spaces. We also demonstrate the
model by prediction of human pose in an ambiguous setting. Our Bayesian
framework allows us to perform disambiguation in a principled manner by
including latent space priors which incorporate the dynamic nature of the data.Comment: ICML201
Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace
Human-in-the-loop manipulation is useful in when autonomous grasping is not
able to deal sufficiently well with corner cases or cannot operate fast enough.
Using the teleoperator's hand as an input device can provide an intuitive
control method but requires mapping between pose spaces which may not be
similar. We propose a low-dimensional and continuous teleoperation subspace
which can be used as an intermediary for mapping between different hand pose
spaces. We present an algorithm to project between pose space and teleoperation
subspace. We use a non-anthropomorphic robot to experimentally prove that it is
possible for teleoperation subspaces to effectively and intuitively enable
teleoperation. In experiments, novice users completed pick and place tasks
significantly faster using teleoperation subspace mapping than they did using
state of the art teleoperation methods.Comment: ICRA 2018, 7 pages, 7 figures, 2 table
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
The interpretation of complex high-dimensional data typically requires the
use of dimensionality reduction techniques to extract explanatory
low-dimensional representations. However, in many real-world problems these
representations may not be sufficient to aid interpretation on their own, and
it would be desirable to interpret the model in terms of the original features
themselves. Our goal is to characterise how feature-level variation depends on
latent low-dimensional representations, external covariates, and non-linear
interactions between the two. In this paper, we propose to achieve this through
a structured kernel decomposition in a hybrid Gaussian Process model which we
call the Covariate Gaussian Process Latent Variable Model (c-GPLVM). We
demonstrate the utility of our model on simulated examples and applications in
disease progression modelling from high-dimensional gene expression data in the
presence of additional phenotypes. In each setting we show how the c-GPLVM can
extract low-dimensional structures from high-dimensional data sets whilst
allowing a breakdown of feature-level variability that is not present in other
commonly used dimensionality reduction approaches
Multi-Source Neural Variational Inference
Learning from multiple sources of information is an important problem in
machine-learning research. The key challenges are learning representations and
formulating inference methods that take into account the complementarity and
redundancy of various information sources. In this paper we formulate a
variational autoencoder based multi-source learning framework in which each
encoder is conditioned on a different information source. This allows us to
relate the sources via the shared latent variables by computing divergence
measures between individual source's posterior approximations. We explore a
variety of options to learn these encoders and to integrate the beliefs they
compute into a consistent posterior approximation. We visualise learned beliefs
on a toy dataset and evaluate our methods for learning shared representations
and structured output prediction, showing trade-offs of learning separate
encoders for each information source. Furthermore, we demonstrate how conflict
detection and redundancy can increase robustness of inference in a multi-source
setting.Comment: AAAI 2019, Association for the Advancement of Artificial Intelligence
(AAAI) 201
Self-Supervised Motion Retargeting with Safety Guarantee
In this paper, we present self-supervised shared latent embedding (S3LE), a
data-driven motion retargeting method that enables the generation of natural
motions in humanoid robots from motion capture data or RGB videos. While it
requires paired data consisting of human poses and their corresponding robot
configurations, it significantly alleviates the necessity of time-consuming
data-collection via novel paired data generating processes. Our self-supervised
learning procedure consists of two steps: automatically generating paired data
to bootstrap the motion retargeting, and learning a projection-invariant
mapping to handle the different expressivity of humans and humanoid robots.
Furthermore, our method guarantees that the generated robot pose is
collision-free and satisfies position limits by utilizing nonparametric
regression in the shared latent space. We demonstrate that our method can
generate expressive robotic motions from both the CMU motion capture database
and YouTube videos