1,342 research outputs found
Parametric t-Distributed Stochastic Exemplar-centered Embedding
Parametric embedding methods such as parametric t-SNE (pt-SNE) have been
widely adopted for data visualization and out-of-sample data embedding without
further computationally expensive optimization or approximation. However, the
performance of pt-SNE is highly sensitive to the hyper-parameter batch size due
to conflicting optimization goals, and often produces dramatically different
embeddings with different choices of user-defined perplexities. To effectively
solve these issues, we present parametric t-distributed stochastic
exemplar-centered embedding methods. Our strategy learns embedding parameters
by comparing given data only with precomputed exemplars, resulting in a cost
function with linear computational and memory complexity, which is further
reduced by noise contrastive samples. Moreover, we propose a shallow embedding
network with high-order feature interactions for data visualization, which is
much easier to tune but produces comparable performance in contrast to a deep
neural network employed by pt-SNE. We empirically demonstrate, using several
benchmark datasets, that our proposed methods significantly outperform pt-SNE
in terms of robustness, visual effects, and quantitative evaluations.Comment: fixed typo
Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction
With the availability of vast amounts of user visitation history on
location-based social networks (LBSN), the problem of Point-of-Interest (POI)
prediction has been extensively studied. However, much of the research has been
conducted solely on voluntary checkin datasets collected from social apps such
as Foursquare or Yelp. While these data contain rich information about
recreational activities (e.g., restaurants, nightlife, and entertainment),
information about more prosaic aspects of people's lives is sparse. This not
only limits our understanding of users' daily routines, but more importantly
the modeling assumptions developed based on characteristics of recreation-based
data may not be suitable for richer check-in data. In this work, we present an
analysis of education "check-in" data using WiFi access logs collected at
Purdue University. We propose a heterogeneous graph-based method to encode the
correlations between users, POIs, and activities, and then jointly learn
embeddings for the vertices. We evaluate our method compared to previous
state-of-the-art POI prediction methods, and show that the assumptions made by
previous methods significantly degrade performance on our data with dense(r)
activity signals. We also show how our learned embeddings could be used to
identify similar students (e.g., for friend suggestions).Comment: published in KDD'1
Classifying document types to enhance search and recommendations in digital libraries
In this paper, we address the problem of classifying documents available from
the global network of (open access) repositories according to their type. We
show that the metadata provided by repositories enabling us to distinguish
research papers, thesis and slides are missing in over 60% of cases. While
these metadata describing document types are useful in a variety of scenarios
ranging from research analytics to improving search and recommender (SR)
systems, this problem has not yet been sufficiently addressed in the context of
the repositories infrastructure. We have developed a new approach for
classifying document types using supervised machine learning based exclusively
on text specific features. We achieve 0.96 F1-score using the random forest and
Adaboost classifiers, which are the best performing models on our data. By
analysing the SR system logs of the CORE [1] digital library aggregator, we
show that users are an order of magnitude more likely to click on research
papers and thesis than on slides. This suggests that using document types as a
feature for ranking/filtering SR results in digital libraries has the potential
to improve user experience.Comment: 12 pages, 21st International Conference on Theory and Practise of
Digital Libraries (TPDL), 2017, Thessaloniki, Greec
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Generative models that learn disentangled representations for different
factors of variation in an image can be very useful for targeted data
augmentation. By sampling from the disentangled latent subspace of interest, we
can efficiently generate new data necessary for a particular task. Learning
disentangled representations is a challenging problem, especially when certain
factors of variation are difficult to label. In this paper, we introduce a
novel architecture that disentangles the latent space into two complementary
subspaces by using only weak supervision in form of pairwise similarity labels.
Inspired by the recent success of cycle-consistent adversarial architectures,
we use cycle-consistency in a variational auto-encoder framework. Our
non-adversarial approach is in contrast with the recent works that combine
adversarial training with auto-encoders to disentangle representations. We show
compelling results of disentangled latent subspaces on three datasets and
compare with recent works that leverage adversarial training
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations
T-distributed stochastic neighbour embedding (t-SNE) is a widely used data
visualisation technique. It differs from its predecessor SNE by the
low-dimensional similarity kernel: the Gaussian kernel was replaced by the
heavy-tailed Cauchy kernel, solving the "crowding problem" of SNE. Here, we
develop an efficient implementation of t-SNE for a -distribution kernel with
an arbitrary degree of freedom , with corresponding to SNE
and corresponding to the standard t-SNE. Using theoretical analysis and
toy examples, we show that can further reduce the crowding problem and
reveal finer cluster structure that is invisible in standard t-SNE. We further
demonstrate the striking effect of heavier-tailed kernels on large real-life
data sets such as MNIST, single-cell RNA-sequencing data, and the HathiTrust
library. We use domain knowledge to confirm that the revealed clusters are
meaningful. Overall, we argue that modifying the tail heaviness of the t-SNE
kernel can yield additional insight into the cluster structure of the data
SNE: Signed Network Embedding
Several network embedding models have been developed for unsigned networks.
However, these models based on skip-gram cannot be applied to signed networks
because they can only deal with one type of link. In this paper, we present our
signed network embedding model called SNE. Our SNE adopts the log-bilinear
model, uses node representations of all nodes along a given path, and further
incorporates two signed-type vectors to capture the positive or negative
relationship of each edge along the path. We conduct two experiments, node
classification and link prediction, on both directed and undirected signed
networks and compare with four baselines including a matrix factorization
method and three state-of-the-art unsigned network embedding models. The
experimental results demonstrate the effectiveness of our signed network
embedding.Comment: To appear in PAKDD 201
Offline Evaluation of Response Prediction in Online Advertising Auctions
Click-through rates and conversion rates are two core ma-chine learning problems in online advertising. The evalua-tion of such systems is often based on traditional supervised learning metrics that ignore how the predictions are used. These predictions are in fact part of bidding systems in on-line advertising auctions. We present here an empirical eval-uation of a metric that is specifically tailored for auctions in online advertising and show that it correlates better than standard metrics with A/B test results
New modification version of principal component analysis with kinetic correlation matrix using kinetic energy
Principle Component Analysis (PCA) is a direct, non-parametric method for extracting pertinent information from confusing data sets. It presents a roadmap for how to reduce a complex data set to a lower dimension to disclose the hidden, simplified structures that often underlie it. However, most PCA methods are not able to realize the desired benefits when they handle real world, and nonlinear data. In this work, a modified version of PCA with kinetic correlation matrix using kinetic energy is proposed. The features of this modified PCA have been assessed on different data sets of air passenger numbers. The results show that the modified version of PCA is more effective in data compression, classes reparability and classification accuracy than using traditional PCA
Γ-stochastic neighbour embedding for feed-forward data visualization
t-distributed Stochastic Neighbour Embedding (t-SNE) is one of the most popular nonlinear dimension reduction techniques used in multiple application domains. In this paper we propose a variation on the embedding neighbourhood distribution, resulting in Γ-SNE, which can construct a feed-forward mapping using an RBF network. We compare the visualizations generated by Γ-SNE with those of t-SNE and provide empirical evidence suggesting the network is capable of robust interpolation and automatic weight regularization
ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids
We introduce an unsupervised feature learning approach that embeds 3D shape
information into a single-view image representation. The main idea is a
self-supervised training objective that, given only a single 2D image, requires
all unseen views of the object to be predictable from learned features. We
implement this idea as an encoder-decoder convolutional neural network. The
network maps an input image of an unknown category and unknown viewpoint to a
latent space, from which a deconvolutional decoder can best "lift" the image to
its complete viewgrid showing the object from all viewing angles. Our
class-agnostic training procedure encourages the representation to capture
fundamental shape primitives and semantic regularities in a data-driven
manner---without manual semantic labels. Our results on two widely-used shape
datasets show 1) our approach successfully learns to perform "mental rotation"
even for objects unseen during training, and 2) the learned latent space is a
powerful representation for object recognition, outperforming several existing
unsupervised feature learning methods.Comment: To appear at ECCV 201
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