212 research outputs found
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark
technique in geoscience and remote sensing (RS). In the past decade, enormous
efforts have been made to process and analyze these hyperspectral (HS) products
mainly by means of seasoned experts. However, with the ever-growing volume of
data, the bulk of costs in manpower and material resources poses new challenges
on reducing the burden of manual labor and improving efficiency. For this
reason, it is, therefore, urgent to develop more intelligent and automatic
approaches for various HS RS applications. Machine learning (ML) tools with
convex optimization have successfully undertaken the tasks of numerous
artificial intelligence (AI)-related applications. However, their ability in
handling complex practical problems remains limited, particularly for HS data,
due to the effects of various spectral variabilities in the process of HS
imaging and the complexity and redundancy of higher dimensional HS signals.
Compared to the convex models, non-convex modeling, which is capable of
characterizing more complex real scenes and providing the model
interpretability technically and theoretically, has been proven to be a
feasible solution to reduce the gap between challenging HS vision tasks and
currently advanced intelligent data processing models
Generalized visual information analysis via tensorial algebra
High order data is modeled using matrices whose entries are numerical arrays of a fixed size. These arrays, called t-scalars, form a commutative ring under the convolution product. Matrices with elements in the ring of t-scalars are referred to as t-matrices. The t-matrices can be scaled, added and multiplied in the usual way. There are t-matrix generalizations of positive matrices, orthogonal matrices and Hermitian symmetric matrices. With the t-matrix model, it is possible to generalize many well known matrix algorithms. In particular, the t-matrices are used to generalize the SVD (Singular Value Decomposition), HOSVD (High Order SVD), PCA (Principal Component Analysis), 2DPCA (two Dimensional PCA) and GCA (Grassmannian Component Analysis). The generalized t-matrix algorithms, namely TSVD, THOSVD, TPCA, T2DPCA and TGCA, are applied to low-rank approximation, reconstruction and supervised classification of images. Experiments show that the t-matrix algorithms compare favourably with standard matrix algorithms
Supervised feature extraction for tensor objects based on maximization of mutual information
a b s t r a c t Several supervised feature extraction methods for tensor objects have been proposed recently, with applications in recognition of objects, faces and handwritten digits. However, the existing methods usually use only second order statistics of the data, typically through calculation of the within-and between-class scatters. Here we propose a method for supervised feature extraction for tensor objects based on maximization of an approximation of mutual information. In this way we utilize information contained in the higher order statistics of the data. Several experiments show that the proposed method results in highly discriminative features
Bilinear Models of Parts and Appearances in Generative Adversarial Networks.
Recent advances in the understanding of Generative Adversarial Networks (GANs) have led to remarkable progress in visual editing and synthesis tasks, capitalizing on the rich semantics that are embedded in the latent spaces of pre-trained GANs. However, existing methods are often tailored to specific GAN architectures and are limited to either discovering global semantic directions that do not facilitate localized control, or require some form of supervision through manually provided regions or segmentation masks. In this light, we present an architecture-agnostic approach that jointly discovers factors representing spatial parts and their appearances in an entirely unsupervised fashion. These factors are obtained by applying a semi-nonnegative tensor factorization on the feature maps, which in turn enables context-aware local image editing with pixel-level control. In addition, we show that the discovered appearance factors correspond to saliency maps that localize concepts of interest, without using any labels. Experiments on a wide range of GAN architectures and datasets show that, in comparison to the state of the art, our method is far more efficient in terms of training time and, most importantly, provides much more accurate localized control
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning
Many problems in machine learning and other fields can be (re)for-mulated as
linearly constrained separable convex programs. In most of the cases, there are
multiple blocks of variables. However, the traditional alternating direction
method (ADM) and its linearized version (LADM, obtained by linearizing the
quadratic penalty term) are for the two-block case and cannot be naively
generalized to solve the multi-block case. So there is great demand on
extending the ADM based methods for the multi-block case. In this paper, we
propose LADM with parallel splitting and adaptive penalty (LADMPSAP) to solve
multi-block separable convex programs efficiently. When all the component
objective functions have bounded subgradients, we obtain convergence results
that are stronger than those of ADM and LADM, e.g., allowing the penalty
parameter to be unbounded and proving the sufficient and necessary conditions}
for global convergence. We further propose a simple optimality measure and
reveal the convergence rate of LADMPSAP in an ergodic sense. For programs with
extra convex set constraints, with refined parameter estimation we devise a
practical version of LADMPSAP for faster convergence. Finally, we generalize
LADMPSAP to handle programs with more difficult objective functions by
linearizing part of the objective function as well. LADMPSAP is particularly
suitable for sparse representation and low-rank recovery problems because its
subproblems have closed form solutions and the sparsity and low-rankness of the
iterates can be preserved during the iteration. It is also highly
parallelizable and hence fits for parallel or distributed computing. Numerical
experiments testify to the advantages of LADMPSAP in speed and numerical
accuracy.Comment: Preliminary version published on Asian Conference on Machine Learning
201
- …