3,699 research outputs found
Discrimination on the Grassmann Manifold: Fundamental Limits of Subspace Classifiers
We present fundamental limits on the reliable classification of linear and
affine subspaces from noisy, linear features. Drawing an analogy between
discrimination among subspaces and communication over vector wireless channels,
we propose two Shannon-inspired measures to characterize asymptotic classifier
performance. First, we define the classification capacity, which characterizes
necessary and sufficient conditions for the misclassification probability to
vanish as the signal dimension, the number of features, and the number of
subspaces to be discerned all approach infinity. Second, we define the
diversity-discrimination tradeoff which, by analogy with the
diversity-multiplexing tradeoff of fading vector channels, characterizes
relationships between the number of discernible subspaces and the
misclassification probability as the noise power approaches zero. We derive
upper and lower bounds on these measures which are tight in many regimes.
Numerical results, including a face recognition application, validate the
results in practice.Comment: 19 pages, 4 figures. Revised submission to IEEE Transactions on
Information Theor
Complexity, BioComplexity, the Connectionist Conjecture and Ontology of Complexity\ud
This paper develops and integrates major ideas and concepts on complexity and biocomplexity - the connectionist conjecture, universal ontology of complexity, irreducible complexity of totality & inherent randomness, perpetual evolution of information, emergence of criticality and equivalence of symmetry & complexity. This paper introduces the Connectionist Conjecture which states that the one and only representation of Totality is the connectionist one i.e. in terms of nodes and edges. This paper also introduces an idea of Universal Ontology of Complexity and develops concepts in that direction. The paper also develops ideas and concepts on the perpetual evolution of information, irreducibility and computability of totality, all in the context of the Connectionist Conjecture. The paper indicates that the control and communication are the prime functionals that are responsible for the symmetry and complexity of complex phenomenon. The paper takes the stand that the phenomenon of life (including its evolution) is probably the nearest to what we can describe with the term “complexity”. The paper also assumes that signaling and communication within the living world and of the living world with the environment creates the connectionist structure of the biocomplexity. With life and its evolution as the substrate, the paper develops ideas towards the ontology of complexity. The paper introduces new complexity theoretic interpretations of fundamental biomolecular parameters. The paper also develops ideas on the methodology to determine the complexity of “true” complex phenomena.\u
Sheaf-Theoretic Stratification Learning from Geometric and Topological Perspectives
In this paper, we investigate a sheaf-theoretic interpretation of
stratification learning from geometric and topological perspectives. Our main
result is the construction of stratification learning algorithms framed in
terms of a sheaf on a partially ordered set with the Alexandroff topology. We
prove that the resulting decomposition is the unique minimal stratification for
which the strata are homogeneous and the given sheaf is constructible. In
particular, when we choose to work with the local homology sheaf, our algorithm
gives an alternative to the local homology transfer algorithm given in Bendich
et al. (2012), and the cohomology stratification algorithm given in Nanda
(2017). Additionally, we give examples of stratifications based on the
geometric techniques of Breiding et al. (2018), illustrating how the
sheaf-theoretic approach can be used to study stratifications from both
topological and geometric perspectives. This approach also points toward future
applications of sheaf theory in the study of topological data analysis by
illustrating the utility of the language of sheaf theory in generalizing
existing algorithms
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