964 research outputs found
Contribution to Graph-based Manifold Learning with Application to Image Categorization.
122 pLos algoritmos de aprendizaje de variedades basados en grafos (Graph,based manifold) son técnicas que han demostrado ser potentes herramientas para la extracción de características y la reducción de la dimensionalidad en los campos de reconomiento de patrones, visión por computador y aprendizaje automático. Estos algoritmos utilizan información basada en las similitudes de pares de muestras y del grafo ponderado resultante para revelar la estructura geométrica intrínseca de la variedad
Contribution to Graph-based Manifold Learning with Application to Image Categorization.
122 pLos algoritmos de aprendizaje de variedades basados en grafos (Graph,based manifold) son técnicas que han demostrado ser potentes herramientas para la extracción de características y la reducción de la dimensionalidad en los campos de reconomiento de patrones, visión por computador y aprendizaje automático. Estos algoritmos utilizan información basada en las similitudes de pares de muestras y del grafo ponderado resultante para revelar la estructura geométrica intrínseca de la variedad
A Survey on Metric Learning for Feature Vectors and Structured Data
The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved
presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new
method
Joint Multi-view Unsupervised Feature Selection and Graph Learning
Despite the recent progress, the existing multi-view unsupervised feature
selection methods mostly suffer from two limitations. First, they generally
utilize either cluster structure or similarity structure to guide the feature
selection, neglecting the possibility of a joint formulation with mutual
benefits. Second, they often learn the similarity structure by either global
structure learning or local structure learning, lacking the capability of graph
learning with both global and local structural awareness. In light of this,
this paper presents a joint multi-view unsupervised feature selection and graph
learning (JMVFG) approach. Particularly, we formulate the multi-view feature
selection with orthogonal decomposition, where each target matrix is decomposed
into a view-specific basis matrix and a view-consistent cluster indicator.
Cross-space locality preservation is incorporated to bridge the cluster
structure learning in the projected space and the similarity learning (i.e.,
graph learning) in the original space. Further, a unified objective function is
presented to enable the simultaneous learning of the cluster structure, the
global and local similarity structures, and the multi-view consistency and
inconsistency, upon which an alternating optimization algorithm is developed
with theoretically proved convergence. Extensive experiments demonstrate the
superiority of our approach for both multi-view feature selection and graph
learning tasks
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