2,506 research outputs found
Unsupervised feature-learning for galaxy SEDs with denoising autoencoders
With the increasing number of deep multi-wavelength galaxy surveys, the
spectral energy distribution (SED) of galaxies has become an invaluable tool
for studying the formation of their structures and their evolution. In this
context, standard analysis relies on simple spectro-photometric selection
criteria based on a few SED colors. If this fully supervised classification
already yielded clear achievements, it is not optimal to extract relevant
information from the data. In this article, we propose to employ very recent
advances in machine learning, and more precisely in feature learning, to derive
a data-driven diagram. We show that the proposed approach based on denoising
autoencoders recovers the bi-modality in the galaxy population in an
unsupervised manner, without using any prior knowledge on galaxy SED
classification. This technique has been compared to principal component
analysis (PCA) and to standard color/color representations. In addition,
preliminary results illustrate that this enables the capturing of extra
physically meaningful information, such as redshift dependence, galaxy mass
evolution and variation over the specific star formation rate. PCA also results
in an unsupervised representation with physical properties, such as mass and
sSFR, although this representation separates out. less other characteristics
(bimodality, redshift evolution) than denoising autoencoders.Comment: 11 pages and 15 figures. To be published in A&
Hybrid Collaborative Filtering with Autoencoders
Collaborative Filtering aims at exploiting the feedback of users to provide
personalised recommendations. Such algorithms look for latent variables in a
large sparse matrix of ratings. They can be enhanced by adding side information
to tackle the well-known cold start problem. While Neu-ral Networks have
tremendous success in image and speech recognition, they have received less
attention in Collaborative Filtering. This is all the more surprising that
Neural Networks are able to discover latent variables in large and
heterogeneous datasets. In this paper, we introduce a Collaborative Filtering
Neural network architecture aka CFN which computes a non-linear Matrix
Factorization from sparse rating inputs and side information. We show
experimentally on the MovieLens and Douban dataset that CFN outper-forms the
state of the art and benefits from side information. We provide an
implementation of the algorithm as a reusable plugin for Torch, a popular
Neural Network framework
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