204,789 research outputs found
Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings
The recovery of the intrinsic geometric structures of data collections is an
important problem in data analysis. Supervised extensions of several manifold
learning approaches have been proposed in the recent years. Meanwhile, existing
methods primarily focus on the embedding of the training data, and the
generalization of the embedding to initially unseen test data is rather
ignored. In this work, we build on recent theoretical results on the
generalization performance of supervised manifold learning algorithms.
Motivated by these performance bounds, we propose a supervised manifold
learning method that computes a nonlinear embedding while constructing a smooth
and regular interpolation function that extends the embedding to the whole data
space in order to achieve satisfactory generalization. The embedding and the
interpolator are jointly learnt such that the Lipschitz regularity of the
interpolator is imposed while ensuring the separation between different
classes. Experimental results on several image data sets show that the proposed
method outperforms traditional classifiers and the supervised dimensionality
reduction algorithms in comparison in terms of classification accuracy in most
settings
Naturalistic Affective Expression Classification by a Multi-Stage Approach Based on Hidden Markov Models
In naturalistic behaviour, the affective states of a person
change at a rate much slower than the typical rate at which video or
audio is recorded (e.g. 25fps for video). Hence, there is a high probability
that consecutive recorded instants of expressions represent a same
affective content. In this paper, a multi-stage automatic affective expression
recognition system is proposed which uses Hidden Markov Models
(HMMs) to take into account this temporal relationship and finalize the
classification process. The hidden states of the HMMs are associated
with the levels of affective dimensions to convert the classification problem
into a best path finding problem in HMM. The system was tested on
the audio data of the Audio/Visual Emotion Challenge (AVEC) datasets
showing performance significantly above that of a one-stage classification
system that does not take into account the temporal relationship, as well
as above the baseline set provided by this Challenge. Due to the generality
of the approach, this system could be applied to other types of
affective modalities
- …