6,955 research outputs found
Quality-based Multimodal Classification Using Tree-Structured Sparsity
Recent studies have demonstrated advantages of information fusion based on
sparsity models for multimodal classification. Among several sparsity models,
tree-structured sparsity provides a flexible framework for extraction of
cross-correlated information from different sources and for enforcing group
sparsity at multiple granularities. However, the existing algorithm only solves
an approximated version of the cost functional and the resulting solution is
not necessarily sparse at group levels. This paper reformulates the
tree-structured sparse model for multimodal classification task. An accelerated
proximal algorithm is proposed to solve the optimization problem, which is an
efficient tool for feature-level fusion among either homogeneous or
heterogeneous sources of information. In addition, a (fuzzy-set-theoretic)
possibilistic scheme is proposed to weight the available modalities, based on
their respective reliability, in a joint optimization problem for finding the
sparsity codes. This approach provides a general framework for quality-based
fusion that offers added robustness to several sparsity-based multimodal
classification algorithms. To demonstrate their efficacy, the proposed methods
are evaluated on three different applications - multiview face recognition,
multimodal face recognition, and target classification.Comment: To Appear in 2014 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR 2014
Multi-view Fuzzy Representation Learning with Rules based Model
Unsupervised multi-view representation learning has been extensively studied
for mining multi-view data. However, some critical challenges remain. On the
one hand, the existing methods cannot explore multi-view data comprehensively
since they usually learn a common representation between views, given that
multi-view data contains both the common information between views and the
specific information within each view. On the other hand, to mine the nonlinear
relationship between data, kernel or neural network methods are commonly used
for multi-view representation learning. However, these methods are lacking in
interpretability. To this end, this paper proposes a new multi-view fuzzy
representation learning method based on the interpretable Takagi-Sugeno-Kang
(TSK) fuzzy system (MVRL_FS). The method realizes multi-view representation
learning from two aspects. First, multi-view data are transformed into a
high-dimensional fuzzy feature space, while the common information between
views and specific information of each view are explored simultaneously.
Second, a new regularization method based on L_(2,1)-norm regression is
proposed to mine the consistency information between views, while the geometric
structure of the data is preserved through the Laplacian graph. Finally,
extensive experiments on many benchmark multi-view datasets are conducted to
validate the superiority of the proposed method.Comment: This work has been accepted by IEEE Transactions on Knowledge and
Data Engineerin
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
- …