21 research outputs found
A Family of Maximum Margin Criterion for Adaptive Learning
In recent years, pattern analysis plays an important role in data mining and
recognition, and many variants have been proposed to handle complicated
scenarios. In the literature, it has been quite familiar with high
dimensionality of data samples, but either such characteristics or large data
have become usual sense in real-world applications. In this work, an improved
maximum margin criterion (MMC) method is introduced firstly. With the new
definition of MMC, several variants of MMC, including random MMC, layered MMC,
2D^2 MMC, are designed to make adaptive learning applicable. Particularly, the
MMC network is developed to learn deep features of images in light of simple
deep networks. Experimental results on a diversity of data sets demonstrate the
discriminant ability of proposed MMC methods are compenent to be adopted in
complicated application scenarios.Comment: 14 page
TextCaps : Handwritten Character Recognition with Very Small Datasets
Many localized languages struggle to reap the benefits of recent advancements
in character recognition systems due to the lack of substantial amount of
labeled training data. This is due to the difficulty in generating large
amounts of labeled data for such languages and inability of deep learning
techniques to properly learn from small number of training samples. We solve
this problem by introducing a technique of generating new training samples from
the existing samples, with realistic augmentations which reflect actual
variations that are present in human hand writing, by adding random controlled
noise to their corresponding instantiation parameters. Our results with a mere
200 training samples per class surpass existing character recognition results
in the EMNIST-letter dataset while achieving the existing results in the three
datasets: EMNIST-balanced, EMNIST-digits, and MNIST. We also develop a strategy
to effectively use a combination of loss functions to improve reconstructions.
Our system is useful in character recognition for localized languages that lack
much labeled training data and even in other related more general contexts such
as object recognition
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