9,976 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
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
Reasoning is essential for the development of large knowledge graphs,
especially for completion, which aims to infer new triples based on existing
ones. Both rules and embeddings can be used for knowledge graph reasoning and
they have their own advantages and difficulties. Rule-based reasoning is
accurate and explainable but rule learning with searching over the graph always
suffers from efficiency due to huge search space. Embedding-based reasoning is
more scalable and efficient as the reasoning is conducted via computation
between embeddings, but it has difficulty learning good representations for
sparse entities because a good embedding relies heavily on data richness. Based
on this observation, in this paper we explore how embedding and rule learning
can be combined together and complement each other's difficulties with their
advantages. We propose a novel framework IterE iteratively learning embeddings
and rules, in which rules are learned from embeddings with proper pruning
strategy and embeddings are learned from existing triples and new triples
inferred by rules. Evaluations on embedding qualities of IterE show that rules
help improve the quality of sparse entity embeddings and their link prediction
results. We also evaluate the efficiency of rule learning and quality of rules
from IterE compared with AMIE+, showing that IterE is capable of generating
high quality rules more efficiently. Experiments show that iteratively learning
embeddings and rules benefit each other during learning and prediction.Comment: This paper is accepted by WWW'1
Completing Low-Rank Matrices with Corrupted Samples from Few Coefficients in General Basis
Subspace recovery from corrupted and missing data is crucial for various
applications in signal processing and information theory. To complete missing
values and detect column corruptions, existing robust Matrix Completion (MC)
methods mostly concentrate on recovering a low-rank matrix from few corrupted
coefficients w.r.t. standard basis, which, however, does not apply to more
general basis, e.g., Fourier basis. In this paper, we prove that the range
space of an matrix with rank can be exactly recovered from few
coefficients w.r.t. general basis, though and the number of corrupted
samples are both as high as . Our model covers
previous ones as special cases, and robust MC can recover the intrinsic matrix
with a higher rank. Moreover, we suggest a universal choice of the
regularization parameter, which is . By our
filtering algorithm, which has theoretical guarantees, we can
further reduce the computational cost of our model. As an application, we also
find that the solutions to extended robust Low-Rank Representation and to our
extended robust MC are mutually expressible, so both our theory and algorithm
can be applied to the subspace clustering problem with missing values under
certain conditions. Experiments verify our theories.Comment: To appear in IEEE Transactions on Information Theor
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