25,926 research outputs found
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
Dyadic prediction methods operate on pairs of objects (dyads), aiming to
infer labels for out-of-sample dyads. We consider the full and almost full cold
start problem in dyadic prediction, a setting that occurs when both objects in
an out-of-sample dyad have not been observed during training, or if one of them
has been observed, but very few times. A popular approach for addressing this
problem is to train a model that makes predictions based on a pairwise feature
representation of the dyads, or, in case of kernel methods, based on a tensor
product pairwise kernel. As an alternative to such a kernel approach, we
introduce a novel two-step learning algorithm that borrows ideas from the
fields of pairwise learning and spectral filtering. We show theoretically that
the two-step method is very closely related to the tensor product kernel
approach, and experimentally that it yields a slightly better predictive
performance. Moreover, unlike existing tensor product kernel methods, the
two-step method allows closed-form solutions for training and parameter
selection via cross-validation estimates both in the full and almost full cold
start settings, making the approach much more efficient and straightforward to
implement
The Emerging Trends of Multi-Label Learning
Exabytes of data are generated daily by humans, leading to the growing need
for new efforts in dealing with the grand challenges for multi-label learning
brought by big data. For example, extreme multi-label classification is an
active and rapidly growing research area that deals with classification tasks
with an extremely large number of classes or labels; utilizing massive data
with limited supervision to build a multi-label classification model becomes
valuable for practical applications, etc. Besides these, there are tremendous
efforts on how to harvest the strong learning capability of deep learning to
better capture the label dependencies in multi-label learning, which is the key
for deep learning to address real-world classification tasks. However, it is
noted that there has been a lack of systemic studies that focus explicitly on
analyzing the emerging trends and new challenges of multi-label learning in the
era of big data. It is imperative to call for a comprehensive survey to fulfill
this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
Many machine learning problems can be formulated as predicting labels for a
pair of objects. Problems of that kind are often referred to as pairwise
learning, dyadic prediction or network inference problems. During the last
decade kernel methods have played a dominant role in pairwise learning. They
still obtain a state-of-the-art predictive performance, but a theoretical
analysis of their behavior has been underexplored in the machine learning
literature.
In this work we review and unify existing kernel-based algorithms that are
commonly used in different pairwise learning settings, ranging from matrix
filtering to zero-shot learning. To this end, we focus on closed-form efficient
instantiations of Kronecker kernel ridge regression. We show that independent
task kernel ridge regression, two-step kernel ridge regression and a linear
matrix filter arise naturally as a special case of Kronecker kernel ridge
regression, implying that all these methods implicitly minimize a squared loss.
In addition, we analyze universality, consistency and spectral filtering
properties. Our theoretical results provide valuable insights in assessing the
advantages and limitations of existing pairwise learning methods.Comment: arXiv admin note: text overlap with arXiv:1606.0427
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
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
We present a novel hybrid algorithm for Bayesian network structure learning,
called H2PC. It first reconstructs the skeleton of a Bayesian network and then
performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
The algorithm is based on divide-and-conquer constraint-based subroutines to
learn the local structure around a target variable. We conduct two series of
experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is
currently the most powerful state-of-the-art algorithm for Bayesian network
structure learning. First, we use eight well-known Bayesian network benchmarks
with various data sizes to assess the quality of the learned structure returned
by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in
terms of goodness of fit to new data and quality of the network structure with
respect to the true dependence structure of the data. Second, we investigate
H2PC's ability to solve the multi-label learning problem. We provide
theoretical results to characterize and identify graphically the so-called
minimal label powersets that appear as irreducible factors in the joint
distribution under the faithfulness condition. The multi-label learning problem
is then decomposed into a series of multi-class classification problems, where
each multi-class variable encodes a label powerset. H2PC is shown to compare
favorably to MMHC in terms of global classification accuracy over ten
multi-label data sets covering different application domains. Overall, our
experiments support the conclusions that local structural learning with H2PC in
the form of local neighborhood induction is a theoretically well-motivated and
empirically effective learning framework that is well suited to multi-label
learning. The source code (in R) of H2PC as well as all data sets used for the
empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author
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