9,453 research outputs found
Robust correlated and individual component analysis
© 1979-2012 IEEE.Recovering correlated and individual components of two, possibly temporally misaligned, sets of data is a fundamental task in disciplines such as image, vision, and behavior computing, with application to problems such as multi-modal fusion (via correlated components), predictive analysis, and clustering (via the individual ones). Here, we study the extraction of correlated and individual components under real-world conditions, namely i) the presence of gross non-Gaussian noise and ii) temporally misaligned data. In this light, we propose a method for the Robust Correlated and Individual Component Analysis (RCICA) of two sets of data in the presence of gross, sparse errors. We furthermore extend RCICA in order to handle temporal incongruities arising in the data. To this end, two suitable optimization problems are solved. The generality of the proposed methods is demonstrated by applying them onto 4 applications, namely i) heterogeneous face recognition, ii) multi-modal feature fusion for human behavior analysis (i.e., audio-visual prediction of interest and conflict), iii) face clustering, and iv) thetemporal alignment of facial expressions. Experimental results on 2 synthetic and 7 real world datasets indicate the robustness and effectiveness of the proposed methodson these application domains, outperforming other state-of-the-art methods in the field
ARES: Adaptive, Reconfigurable, Erasure coded, atomic Storage
Atomicity or strong consistency is one of the fundamental, most intuitive,
and hardest to provide primitives in distributed shared memory emulations. To
ensure survivability, scalability, and availability of a storage service in the
presence of failures, traditional approaches for atomic memory emulation, in
message passing environments, replicate the objects across multiple servers.
Compared to replication based algorithms, erasure code-based atomic memory
algorithms has much lower storage and communication costs, but usually, they
are harder to design. The difficulty of designing atomic memory algorithms
further grows, when the set of servers may be changed to ensure survivability
of the service over software and hardware upgrades, while avoiding service
interruptions. Atomic memory algorithms for performing server reconfiguration,
in the replicated systems, are very few, complex, and are still part of an
active area of research; reconfigurations of erasure-code based algorithms are
non-existent.
In this work, we present ARES, an algorithmic framework that allows
reconfiguration of the underlying servers, and is particularly suitable for
erasure-code based algorithms emulating atomic objects. ARES introduces new
configurations while keeping the service available. To use with ARES we also
propose a new, and to our knowledge, the first two-round erasure code based
algorithm TREAS, for emulating multi-writer, multi-reader (MWMR) atomic objects
in asynchronous, message-passing environments, with near-optimal communication
and storage costs. Our algorithms can tolerate crash failures of any client and
some fraction of servers, and yet, guarantee safety and liveness property.
Moreover, by bringing together the advantages of ARES and TREAS, we propose an
optimized algorithm where new configurations can be installed without the
objects values passing through the reconfiguration clients
Robust correlated and individual component analysis
Recovering correlated and individual components of two, possibly temporally misaligned, sets of data is a fundamental task in disciplines such as image, vision, and behavior computing, with application to problems such as multi-modal fusion (via correlated components), predictive analysis, and clustering (via the individual ones). Here, we study the extraction of correlated and individual components under real-world conditions, namely i) the presence of gross non-Gaussian noise and ii) temporally misaligned data. In this light, we propose a method for the Robust Correlated and Individual Component Analysis (RCICA) of two sets of data in the presence of gross, sparse errors. We furthermore extend RCICA in order to handle temporal incongruities arising in the data. To this end, two suitable optimization problems are solved. The generality of the proposed methods is demonstrated by applying them onto 4 applications, namely i) heterogeneous face recognition, ii) multi-modal feature fusion for human behavior analysis (i.e., audio-visual prediction of interest and conflict), iii) face clustering, and iv) the temporal alignment of facial expressions. Experimental results on 2 synthetic and 7 real world datasets indicate the robustness and effectiveness of the proposed methods on these application domains, outperforming other state-of-the-art methods in the field
Correlated-Spaces Regression for Learning Continuous Emotion Dimensions
Adopting continuous dimensional annotations for affective analysis has been gaining rising attention by researchers over the past years. Due to the idiosyncratic nature of this problem, many subproblems have been identified, spanning from the fusion of multiple continuous annotations to exploiting output-correlations amongst emotion dimensions. In this paper, we firstly empirically answer several important questions which have found partial or no answer at all so far in related literature. In more detail, we study the correlation of each emotion dimension (i) with respect to other emotion dimensions, (ii) to basic emotions (e.g., happiness, anger). As a measure for comparison, we use video and audio features. Interestingly enough, we find that (i) each emotion dimension is more correlated with other emotion dimensions rather than with face and audio features, and similarly (ii) that each basic emotion is more correlated with emotion dimensions than with audio and video features. A similar conclusion holds for discrete emotions which are found to be highly correlated to emotion dimensions as compared to audio and/or video features. Motivated by these findings, we present a novel regression algorithm (Correlated-Spaces Regression, CSR), inspired by Canonical Correlation Analysis (CCA) which learns output-correlations and performs supervised dimensionality reduction and multimodal fusion by (i) projecting features extracted from all modalities and labels onto a common space where their inter-correlation is maximised and (ii) learning mappings from the projected feature space onto the projected, uncorrelated label space
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
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