1,471 research outputs found
Semisupervised Autoencoder for Sentiment Analysis
In this paper, we investigate the usage of autoencoders in modeling textual
data. Traditional autoencoders suffer from at least two aspects: scalability
with the high dimensionality of vocabulary size and dealing with
task-irrelevant words. We address this problem by introducing supervision via
the loss function of autoencoders. In particular, we first train a linear
classifier on the labeled data, then define a loss for the autoencoder with the
weights learned from the linear classifier. To reduce the bias brought by one
single classifier, we define a posterior probability distribution on the
weights of the classifier, and derive the marginalized loss of the autoencoder
with Laplace approximation. We show that our choice of loss function can be
rationalized from the perspective of Bregman Divergence, which justifies the
soundness of our model. We evaluate the effectiveness of our model on six
sentiment analysis datasets, and show that our model significantly outperforms
all the competing methods with respect to classification accuracy. We also show
that our model is able to take advantage of unlabeled dataset and get improved
performance. We further show that our model successfully learns highly
discriminative feature maps, which explains its superior performance.Comment: To appear in AAAI 201
Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders
Radar images of humans and other concealed objects are considerably distorted
by attenuation, refraction and multipath clutter in indoor through-wall
environments. While several methods have been proposed for removing target
independent static and dynamic clutter, there still remain considerable
challenges in mitigating target dependent clutter especially when the knowledge
of the exact propagation characteristics or analytical framework is
unavailable. In this work we focus on mitigating wall effects using a machine
learning based solution -- denoising autoencoders -- that does not require
prior information of the wall parameters or room geometry. Instead, the method
relies on the availability of a large volume of training radar images gathered
in through-wall conditions and the corresponding clean images captured in
line-of-sight conditions. During the training phase, the autoencoder learns how
to denoise the corrupted through-wall images in order to resemble the free
space images. We have validated the performance of the proposed solution for
both static and dynamic human subjects. The frontal radar images of static
targets are obtained by processing wideband planar array measurement data with
two-dimensional array and range processing. The frontal radar images of dynamic
targets are simulated using narrowband planar array data processed with
two-dimensional array and Doppler processing. In both simulation and
measurement processes, we incorporate considerable diversity in the target and
propagation conditions. Our experimental results, from both simulation and
measurement data, show that the denoised images are considerably more similar
to the free-space images when compared to the original through-wall images
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