1,119 research outputs found
Semi-Supervised Sparse Coding
Sparse coding approximates the data sample as a sparse linear combination of
some basic codewords and uses the sparse codes as new presentations. In this
paper, we investigate learning discriminative sparse codes by sparse coding in
a semi-supervised manner, where only a few training samples are labeled. By
using the manifold structure spanned by the data set of both labeled and
unlabeled samples and the constraints provided by the labels of the labeled
samples, we learn the variable class labels for all the samples. Furthermore,
to improve the discriminative ability of the learned sparse codes, we assume
that the class labels could be predicted from the sparse codes directly using a
linear classifier. By solving the codebook, sparse codes, class labels and
classifier parameters simultaneously in a unified objective function, we
develop a semi-supervised sparse coding algorithm. Experiments on two
real-world pattern recognition problems demonstrate the advantage of the
proposed methods over supervised sparse coding methods on partially labeled
data sets
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
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
Semi-supervised classification of human actions based on Neural Networks
In this paper, we propose a novel algorithm for Single-hidden Layer Feedforward Neural networks training which is able to exploit information coming from both labeled and unlabeled data for semi-supervised action classification. We extendthe Extreme Learning Machine algorithm by incorporating appropriate regularization terms describing geometric properties and discrimination criteria of the training data representation in the ELM space to this end. The proposed algorithm is evaluated on human action recognition, where its performance is compared with that of other (semi-)supervised classification schemes. Experimental results on two publicly available action recognition databases denote its effectiveness
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
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