163,483 research outputs found
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
Dimensionality reduction and manifold learning methods such as t-Distributed
Stochastic Neighbor Embedding (t-SNE) are routinely used to map
high-dimensional data into a 2-dimensional space to visualize and explore the
data. However, two dimensions are typically insufficient to capture all
structure in the data, the salient structure is often already known, and it is
not obvious how to extract the remaining information in a similarly effective
manner. To fill this gap, we introduce \emph{conditional t-SNE} (ct-SNE), a
generalization of t-SNE that discounts prior information from the embedding in
the form of labels. To achieve this, we propose a conditioned version of the
t-SNE objective, obtaining a single, integrated, and elegant method. ct-SNE has
one extra parameter over t-SNE; we investigate its effects and show how to
efficiently optimize the objective. Factoring out prior knowledge allows
complementary structure to be captured in the embedding, providing new
insights. Qualitative and quantitative empirical results on synthetic and
(large) real data show ct-SNE is effective and achieves its goal
Efficient Deep Clustering of Human Activities and How to Improve Evaluation
There has been much recent research on human activity re\-cog\-ni\-tion
(HAR), due to the proliferation of wearable sensors in watches and phones, and
the advances of deep learning methods, which avoid the need to manually extract
features from raw sensor signals. A significant disadvantage of deep learning
applied to HAR is the need for manually labelled training data, which is
especially difficult to obtain for HAR datasets. Progress is starting to be
made in the unsupervised setting, in the form of deep HAR clustering models,
which can assign labels to data without having been given any labels to train
on, but there are problems with evaluating deep HAR clustering models, which
makes assessing the field and devising new methods difficult. In this paper, we
highlight several distinct problems with how deep HAR clustering models are
evaluated, describing these problems in detail and conducting careful
experiments to explicate the effect that they can have on results. We then
discuss solutions to these problems, and suggest standard evaluation settings
for future deep HAR clustering models. Additionally, we present a new deep
clustering model for HAR. When tested under our proposed settings, our model
performs better than (or on par with) existing models, while also being more
efficient and better able to scale to more complex datasets by avoiding the
need for an autoencoder
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