773 research outputs found
Expanding the Family of Grassmannian Kernels: An Embedding Perspective
Modeling videos and image-sets as linear subspaces has proven beneficial for
many visual recognition tasks. However, it also incurs challenges arising from
the fact that linear subspaces do not obey Euclidean geometry, but lie on a
special type of Riemannian manifolds known as Grassmannian. To leverage the
techniques developed for Euclidean spaces (e.g, support vector machines) with
subspaces, several recent studies have proposed to embed the Grassmannian into
a Hilbert space by making use of a positive definite kernel. Unfortunately,
only two Grassmannian kernels are known, none of which -as we will show- is
universal, which limits their ability to approximate a target function
arbitrarily well. Here, we introduce several positive definite Grassmannian
kernels, including universal ones, and demonstrate their superiority over
previously-known kernels in various tasks, such as classification, clustering,
sparse coding and hashing
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold
Although Deep Learning (DL) has achieved success in complex Artificial
Intelligence (AI) tasks, it suffers from various notorious problems (e.g.,
feature redundancy, and vanishing or exploding gradients), since updating
parameters in Euclidean space cannot fully exploit the geometric structure of
the solution space. As a promising alternative solution, Riemannian-based DL
uses geometric optimization to update parameters on Riemannian manifolds and
can leverage the underlying geometric information. Accordingly, this article
presents a comprehensive survey of applying geometric optimization in DL. At
first, this article introduces the basic procedure of the geometric
optimization, including various geometric optimizers and some concepts of
Riemannian manifold. Subsequently, this article investigates the application of
geometric optimization in different DL networks in various AI tasks, e.g.,
convolution neural network, recurrent neural network, transfer learning, and
optimal transport. Additionally, typical public toolboxes that implement
optimization on manifold are also discussed. Finally, this article makes a
performance comparison between different deep geometric optimization methods
under image recognition scenarios.Comment: 41 page
Multi-Word Terminology Extraction and Its Role in Document Embedding
Automated terminology extraction is a crucial task in natural language processing and ontology construction. Termhood can be inferred using linguistic and statistic techniques. This thesis focuses on the statistic methods. Inspired by feature selection techniques in documents classification, we experiment with a variety of metrics including PMI (point-wise mutual information), MI (mutual information), and Chi-squared. We find that PMI is in favour of identifying top keywords in a domain, but Chi-squared can recognize more keywords overall. Based on this observation, we propose a hybrid approach, called HMI, that combines the best of PMI and Chi-squared. HMI outperforms both PMI and Chi-squared. The result is verified by comparing overlapping between the extracted keywords and the author-identified keywords in arXiv data. When the corpora are computer science and physics papers, the top-100 hit rate can reach 0.96 for HMI. We also demonstrate that terminologies can improve documents embeddings. In this experiment, we treat machine-identified multi-word terminologies with one word. Then we use the transformed text as input for the document embedding. Compared with the representations learnt from unigrams only, we observe a performance improvement over 9.41% for F1 score in arXiv data on document classification tasks
A survey of face recognition techniques under occlusion
The limited capacity to recognize faces under occlusions is a long-standing
problem that presents a unique challenge for face recognition systems and even
for humans. The problem regarding occlusion is less covered by research when
compared to other challenges such as pose variation, different expressions,
etc. Nevertheless, occluded face recognition is imperative to exploit the full
potential of face recognition for real-world applications. In this paper, we
restrict the scope to occluded face recognition. First, we explore what the
occlusion problem is and what inherent difficulties can arise. As a part of
this review, we introduce face detection under occlusion, a preliminary step in
face recognition. Second, we present how existing face recognition methods cope
with the occlusion problem and classify them into three categories, which are
1) occlusion robust feature extraction approaches, 2) occlusion aware face
recognition approaches, and 3) occlusion recovery based face recognition
approaches. Furthermore, we analyze the motivations, innovations, pros and
cons, and the performance of representative approaches for comparison. Finally,
future challenges and method trends of occluded face recognition are thoroughly
discussed
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