81 research outputs found
Label Propagation for Learning with Label Proportions
Learning with Label Proportions (LLP) is the problem of recovering the
underlying true labels given a dataset when the data is presented in the form
of bags. This paradigm is particularly suitable in contexts where providing
individual labels is expensive and label aggregates are more easily obtained.
In the healthcare domain, it is a burden for a patient to keep a detailed diary
of their daily routines, but often they will be amenable to provide higher
level summaries of daily behavior. We present a novel and efficient graph-based
algorithm that encourages local smoothness and exploits the global structure of
the data, while preserving the `mass' of each bag.Comment: Accepted to MLSP 201
Distributed Low-rank Subspace Segmentation
Vision problems ranging from image clustering to motion segmentation to
semi-supervised learning can naturally be framed as subspace segmentation
problems, in which one aims to recover multiple low-dimensional subspaces from
noisy and corrupted input data. Low-Rank Representation (LRR), a convex
formulation of the subspace segmentation problem, is provably and empirically
accurate on small problems but does not scale to the massive sizes of modern
vision datasets. Moreover, past work aimed at scaling up low-rank matrix
factorization is not applicable to LRR given its non-decomposable constraints.
In this work, we propose a novel divide-and-conquer algorithm for large-scale
subspace segmentation that can cope with LRR's non-decomposable constraints and
maintains LRR's strong recovery guarantees. This has immediate implications for
the scalability of subspace segmentation, which we demonstrate on a benchmark
face recognition dataset and in simulations. We then introduce novel
applications of LRR-based subspace segmentation to large-scale semi-supervised
learning for multimedia event detection, concept detection, and image tagging.
In each case, we obtain state-of-the-art results and order-of-magnitude speed
ups
Aplicação de técnicas de classificação semissupervisionada para análise de séries multitemporais de imagens de satélite.
Este trabalho apresenta uma comparação de dois algoritmos de classificação semissupervisionada utilizados para auxiliar na identificação de áreas de cultivo de cana-de-açúcar, uma importante commoditie brasileira. As técnicas foram incorporadas ao software SatImagExplorer, que foi desenvolvido para auxiliar na extração de séries temporais de imagens de satélite (CHINO; ROMANI; TRAINA, 2010). Os resultados indicam que ambas as técnicas apresentaram resultados satisfatórios para classificação de diferentes classes usando séries de imagens de baixa resolução espacial.SBSR 2013
Relative Comparison Kernel Learning with Auxiliary Kernels
In this work we consider the problem of learning a positive semidefinite
kernel matrix from relative comparisons of the form: "object A is more similar
to object B than it is to C", where comparisons are given by humans. Existing
solutions to this problem assume many comparisons are provided to learn a high
quality kernel. However, this can be considered unrealistic for many real-world
tasks since relative assessments require human input, which is often costly or
difficult to obtain. Because of this, only a limited number of these
comparisons may be provided. In this work, we explore methods for aiding the
process of learning a kernel with the help of auxiliary kernels built from more
easily extractable information regarding the relationships among objects. We
propose a new kernel learning approach in which the target kernel is defined as
a conic combination of auxiliary kernels and a kernel whose elements are
learned directly. We formulate a convex optimization to solve for this target
kernel that adds only minor overhead to methods that use no auxiliary
information. Empirical results show that in the presence of few training
relative comparisons, our method can learn kernels that generalize to more
out-of-sample comparisons than methods that do not utilize auxiliary
information, as well as similar methods that learn metrics over objects
- …