45,572 research outputs found
Tree-guided group lasso for multi-response regression with structured sparsity, with an application to eQTL mapping
We consider the problem of estimating a sparse multi-response regression
function, with an application to expression quantitative trait locus (eQTL)
mapping, where the goal is to discover genetic variations that influence
gene-expression levels. In particular, we investigate a shrinkage technique
capable of capturing a given hierarchical structure over the responses, such as
a hierarchical clustering tree with leaf nodes for responses and internal nodes
for clusters of related responses at multiple granularity, and we seek to
leverage this structure to recover covariates relevant to each
hierarchically-defined cluster of responses. We propose a tree-guided group
lasso, or tree lasso, for estimating such structured sparsity under
multi-response regression by employing a novel penalty function constructed
from the tree. We describe a systematic weighting scheme for the overlapping
groups in the tree-penalty such that each regression coefficient is penalized
in a balanced manner despite the inhomogeneous multiplicity of group
memberships of the regression coefficients due to overlaps among groups. For
efficient optimization, we employ a smoothing proximal gradient method that was
originally developed for a general class of structured-sparsity-inducing
penalties. Using simulated and yeast data sets, we demonstrate that our method
shows a superior performance in terms of both prediction errors and recovery of
true sparsity patterns, compared to other methods for learning a
multivariate-response regression.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS549 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Audio-visual foreground extraction for event characterization
This paper presents a new method able to integrate audio and visual information for scene analysis in a typical surveillance scenario, using only one camera and one monaural microphone. Visual information is analyzed by a standard visual background/foreground (BG/FG) modelling module, enhanced with a novelty detection stage, and coupled with an audio BG/FG modelling scheme. The audiovisual association is performed on-line, by exploiting the concept of synchrony. Experimental tests carrying out classification and clustering of events show all the potentialities of the proposed approach, also in comparison with the results obtained by using the single modalities
Action Recognition in Video Using Sparse Coding and Relative Features
This work presents an approach to category-based action recognition in video
using sparse coding techniques. The proposed approach includes two main
contributions: i) A new method to handle intra-class variations by decomposing
each video into a reduced set of representative atomic action acts or
key-sequences, and ii) A new video descriptor, ITRA: Inter-Temporal Relational
Act Descriptor, that exploits the power of comparative reasoning to capture
relative similarity relations among key-sequences. In terms of the method to
obtain key-sequences, we introduce a loss function that, for each video, leads
to the identification of a sparse set of representative key-frames capturing
both, relevant particularities arising in the input video, as well as relevant
generalities arising in the complete class collection. In terms of the method
to obtain the ITRA descriptor, we introduce a novel scheme to quantify relative
intra and inter-class similarities among local temporal patterns arising in the
videos. The resulting ITRA descriptor demonstrates to be highly effective to
discriminate among action categories. As a result, the proposed approach
reaches remarkable action recognition performance on several popular benchmark
datasets, outperforming alternative state-of-the-art techniques by a large
margin.Comment: Accepted to CVPR 201
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models
A control algorithm for autonomous optimization of extracellular recordings
This paper develops a control algorithm that can autonomously position an electrode so as to find and then maintain an optimal extracellular recording position. The algorithm was developed and tested in a two-neuron computational model representative of the cells found in cerebral cortex. The algorithm is based on a stochastic optimization of a suitably defined signal quality metric and is shown capable of finding the optimal recording position along representative sampling directions, as well as maintaining the optimal signal quality in the face of modeled tissue movements. The application of the algorithm to acute neurophysiological recording experiments and its potential implications to chronic recording electrode arrays are discussed
Non-Negative Local Sparse Coding for Subspace Clustering
Subspace sparse coding (SSC) algorithms have proven to be beneficial to
clustering problems. They provide an alternative data representation in which
the underlying structure of the clusters can be better captured. However, most
of the research in this area is mainly focused on enhancing the sparse coding
part of the problem. In contrast, we introduce a novel objective term in our
proposed SSC framework which focuses on the separability of data points in the
coding space. We also provide mathematical insights into how this
local-separability term improves the clustering result of the SSC framework.
Our proposed non-linear local SSC algorithm (NLSSC) also benefits from the
efficient choice of its sparsity terms and constraints. The NLSSC algorithm is
also formulated in the kernel-based framework (NLKSSC) which can represent the
nonlinear structure of data. In addition, we address the possibility of having
redundancies in sparse coding results and its negative effect on graph-based
clustering problems. We introduce the link-restore post-processing step to
improve the representation graph of non-negative SSC algorithms such as ours.
Empirical evaluations on well-known clustering benchmarks show that our
proposed NLSSC framework results in better clusterings compared to the
state-of-the-art baselines and demonstrate the effectiveness of the
link-restore post-processing in improving the clustering accuracy via
correcting the broken links of the representation graph.Comment: 15 pages, IDA 2018 conferenc
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