249 research outputs found
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
This paper proposes a novel deep learning framework named
bidirectional-convolutional long short term memory (Bi-CLSTM) network to
automatically learn the spectral-spatial feature from hyperspectral images
(HSIs). In the network, the issue of spectral feature extraction is considered
as a sequence learning problem, and a recurrent connection operator across the
spectral domain is used to address it. Meanwhile, inspired from the widely used
convolutional neural network (CNN), a convolution operator across the spatial
domain is incorporated into the network to extract the spatial feature.
Besides, to sufficiently capture the spectral information, a bidirectional
recurrent connection is proposed. In the classification phase, the learned
features are concatenated into a vector and fed to a softmax classifier via a
fully-connected operator. To validate the effectiveness of the proposed
Bi-CLSTM framework, we compare it with several state-of-the-art methods,
including the CNN framework, on three widely used HSIs. The obtained results
show that Bi-CLSTM can improve the classification performance as compared to
other methods
Robust Low-Rank Subspace Segmentation with Semidefinite Guarantees
Recently there is a line of research work proposing to employ Spectral
Clustering (SC) to segment (group){Throughout the paper, we use segmentation,
clustering, and grouping, and their verb forms, interchangeably.}
high-dimensional structural data such as those (approximately) lying on
subspaces {We follow {liu2010robust} and use the term "subspace" to denote both
linear subspaces and affine subspaces. There is a trivial conversion between
linear subspaces and affine subspaces as mentioned therein.} or low-dimensional
manifolds. By learning the affinity matrix in the form of sparse
reconstruction, techniques proposed in this vein often considerably boost the
performance in subspace settings where traditional SC can fail. Despite the
success, there are fundamental problems that have been left unsolved: the
spectrum property of the learned affinity matrix cannot be gauged in advance,
and there is often one ugly symmetrization step that post-processes the
affinity for SC input. Hence we advocate to enforce the symmetric positive
semidefinite constraint explicitly during learning (Low-Rank Representation
with Positive SemiDefinite constraint, or LRR-PSD), and show that factually it
can be solved in an exquisite scheme efficiently instead of general-purpose SDP
solvers that usually scale up poorly. We provide rigorous mathematical
derivations to show that, in its canonical form, LRR-PSD is equivalent to the
recently proposed Low-Rank Representation (LRR) scheme {liu2010robust}, and
hence offer theoretic and practical insights to both LRR-PSD and LRR, inviting
future research. As per the computational cost, our proposal is at most
comparable to that of LRR, if not less. We validate our theoretic analysis and
optimization scheme by experiments on both synthetic and real data sets.Comment: 10 pages, 4 figures. Accepted by ICDM Workshop on Optimization Based
Methods for Emerging Data Mining Problems (OEDM), 2010. Main proof simplified
and typos corrected. Experimental data slightly adde
Information-Theoretic Measures for Objective Evaluation of Classifications
This work presents a systematic study of objective evaluations of abstaining
classifications using Information-Theoretic Measures (ITMs). First, we define
objective measures for which they do not depend on any free parameter. This
definition provides technical simplicity for examining "objectivity" or
"subjectivity" directly to classification evaluations. Second, we propose
twenty four normalized ITMs, derived from either mutual information,
divergence, or cross-entropy, for investigation. Contrary to conventional
performance measures that apply empirical formulas based on users' intuitions
or preferences, the ITMs are theoretically more sound for realizing objective
evaluations of classifications. We apply them to distinguish "error types" and
"reject types" in binary classifications without the need for input data of
cost terms. Third, to better understand and select the ITMs, we suggest three
desirable features for classification assessment measures, which appear more
crucial and appealing from the viewpoint of classification applications. Using
these features as "meta-measures", we can reveal the advantages and limitations
of ITMs from a higher level of evaluation knowledge. Numerical examples are
given to corroborate our claims and compare the differences among the proposed
measures. The best measure is selected in terms of the meta-measures, and its
specific properties regarding error types and reject types are analytically
derived.Comment: 25 Pages, 1 Figure, 10 Table
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