3,773 research outputs found
Effective Discriminative Feature Selection with Non-trivial Solutions
Feature selection and feature transformation, the two main ways to reduce
dimensionality, are often presented separately. In this paper, a feature
selection method is proposed by combining the popular transformation based
dimensionality reduction method Linear Discriminant Analysis (LDA) and sparsity
regularization. We impose row sparsity on the transformation matrix of LDA
through -norm regularization to achieve feature selection, and
the resultant formulation optimizes for selecting the most discriminative
features and removing the redundant ones simultaneously. The formulation is
extended to the -norm regularized case: which is more likely to
offer better sparsity when . Thus the formulation is a better
approximation to the feature selection problem. An efficient algorithm is
developed to solve the -norm based optimization problem and it is
proved that the algorithm converges when . Systematical experiments
are conducted to understand the work of the proposed method. Promising
experimental results on various types of real-world data sets demonstrate the
effectiveness of our algorithm
DSL: Discriminative Subgraph Learning via Sparse Self-Representation
The goal in network state prediction (NSP) is to classify the global state
(label) associated with features embedded in a graph. This graph structure
encoding feature relationships is the key distinctive aspect of NSP compared to
classical supervised learning. NSP arises in various applications: gene
expression samples embedded in a protein-protein interaction (PPI) network,
temporal snapshots of infrastructure or sensor networks, and fMRI coherence
network samples from multiple subjects to name a few. Instances from these
domains are typically ``wide'' (more features than samples), and thus, feature
sub-selection is required for robust and generalizable prediction. How to best
employ the network structure in order to learn succinct connected subgraphs
encompassing the most discriminative features becomes a central challenge in
NSP. Prior work employs connected subgraph sampling or graph smoothing within
optimization frameworks, resulting in either large variance of quality or weak
control over the connectivity of selected subgraphs.
In this work we propose an optimization framework for discriminative subgraph
learning (DSL) which simultaneously enforces (i) sparsity, (ii) connectivity
and (iii) high discriminative power of the resulting subgraphs of features. Our
optimization algorithm is a single-step solution for the NSP and the associated
feature selection problem. It is rooted in the rich literature on
maximal-margin optimization, spectral graph methods and sparse subspace
self-representation. DSL simultaneously ensures solution interpretability and
superior predictive power (up to 16% improvement in challenging instances
compared to baselines), with execution times up to an hour for large instances.Comment: 9 page
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Generative Adversarial Networks (GANs) is a novel class of deep generative
models which has recently gained significant attention. GANs learns complex and
high-dimensional distributions implicitly over images, audio, and data.
However, there exists major challenges in training of GANs, i.e., mode
collapse, non-convergence and instability, due to inappropriate design of
network architecture, use of objective function and selection of optimization
algorithm. Recently, to address these challenges, several solutions for better
design and optimization of GANs have been investigated based on techniques of
re-engineered network architectures, new objective functions and alternative
optimization algorithms. To the best of our knowledge, there is no existing
survey that has particularly focused on broad and systematic developments of
these solutions. In this study, we perform a comprehensive survey of the
advancements in GANs design and optimization solutions proposed to handle GANs
challenges. We first identify key research issues within each design and
optimization technique and then propose a new taxonomy to structure solutions
by key research issues. In accordance with the taxonomy, we provide a detailed
discussion on different GANs variants proposed within each solution and their
relationships. Finally, based on the insights gained, we present the promising
research directions in this rapidly growing field.Comment: 42 pages, Figure 13, Table
Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
Feature selection is essential for effective visual recognition. We propose
an efficient joint classifier learning and feature selection method that
discovers sparse, compact representations of input features from a vast sea of
candidates, with an almost unsupervised formulation. Our method requires only
the following knowledge, which we call the \emph{feature sign}---whether or not
a particular feature has on average stronger values over positive samples than
over negatives. We show how this can be estimated using as few as a single
labeled training sample per class. Then, using these feature signs, we extend
an initial supervised learning problem into an (almost) unsupervised clustering
formulation that can incorporate new data without requiring ground truth
labels. Our method works both as a feature selection mechanism and as a fully
competitive classifier. It has important properties, low computational cost and
excellent accuracy, especially in difficult cases of very limited training
data. We experiment on large-scale recognition in video and show superior speed
and performance to established feature selection approaches such as AdaBoost,
Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.Comment: arXiv admin note: text overlap with arXiv:1411.771
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