24,057 research outputs found
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data
There are threefold challenges in emotion recognition. First, it is difficult
to recognize human's emotional states only considering a single modality.
Second, it is expensive to manually annotate the emotional data. Third,
emotional data often suffers from missing modalities due to unforeseeable
sensor malfunction or configuration issues. In this paper, we address all these
problems under a novel multi-view deep generative framework. Specifically, we
propose to model the statistical relationships of multi-modality emotional data
using multiple modality-specific generative networks with a shared latent
space. By imposing a Gaussian mixture assumption on the posterior approximation
of the shared latent variables, our framework can learn the joint deep
representation from multiple modalities and evaluate the importance of each
modality simultaneously. To solve the labeled-data-scarcity problem, we extend
our multi-view model to semi-supervised learning scenario by casting the
semi-supervised classification problem as a specialized missing data imputation
task. To address the missing-modality problem, we further extend our
semi-supervised multi-view model to deal with incomplete data, where a missing
view is treated as a latent variable and integrated out during inference. This
way, the proposed overall framework can utilize all available (both labeled and
unlabeled, as well as both complete and incomplete) data to improve its
generalization ability. The experiments conducted on two real multi-modal
emotion datasets demonstrated the superiority of our framework.Comment: arXiv admin note: text overlap with arXiv:1704.07548, 2018 ACM
Multimedia Conference (MM'18
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
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
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