21,573 research outputs found
A divide-and-conquer algorithm for binary matrix completion
We propose an algorithm for low rank matrix completion for matrices with
binary entries which obtains explicit binary factors. Our algorithm, which we
call TBMC (\emph{Tiling for Binary Matrix Completion}), gives interpretable
output in the form of binary factors which represent a decomposition of the
matrix into tiles. Our approach is inspired by a popular algorithm from the
data mining community called PROXIMUS: it adopts the same recursive
partitioning approach while extending to missing data. The algorithm relies
upon rank-one approximations of incomplete binary matrices, and we propose a
linear programming (LP) approach for solving this subproblem. We also prove a
-approximation result for the LP approach which holds for any level of
subsampling and for any subsampling pattern. Our numerical experiments show
that TBMC outperforms existing methods on recommender systems arising in the
context of real datasets.Comment: 14 pages,4 figure
Multispectral snapshot demosaicing via non-convex matrix completion
Snapshot mosaic multispectral imagery acquires an undersampled data cube by
acquiring a single spectral measurement per spatial pixel. Sensors which
acquire frequencies, therefore, suffer from severe undersampling of
the full data cube. We show that the missing entries can be accurately imputed
using non-convex techniques from sparse approximation and matrix completion
initialised with traditional demosaicing algorithms. In particular, we observe
the peak signal-to-noise ratio can typically be improved by 2 to 5 dB over
current state-of-the-art methods when simulating a mosaic sensor
measuring both high and low altitude urban and rural scenes as well as
ground-based scenes.Comment: 5 pages, 2 figures, 1 tabl
Sparse and low rank approximations for action recognition
Action recognition is crucial area of research in computer vision with wide range of
applications in surveillance, patient-monitoring systems, video indexing, Human-
Computer Interaction and many more. These applications require automated
action recognition. Robust classification methods are sought-after despite influential
research in this field over past decade. The data resources have grown
tremendously owing to the advances in the digital revolution which cannot be
compared to the meagre resources in the past. The main limitation on a system
when dealing with video data is the computational burden due to large dimensions
and data redundancy. Sparse and low rank approximation methods have evolved
recently which aim at concise and meaningful representation of data. This thesis
explores the application of sparse and low rank approximation methods in the
context of video data classification with the following contributions.
1. An approach for solving the problem of action and gesture classification is
proposed within the sparse representation domain, effectively dealing with
large feature dimensions,
2. Low rank matrix completion approach is proposed to jointly classify more
than one action
3. Deep features are proposed for robust classification of multiple actions
within matrix completion framework which can handle data deficiencies.
This thesis starts with the applicability of sparse representations based classifi-
cation methods to the problem of action and gesture recognition. Random projection
is used to reduce the dimensionality of the features. These are referred
to as compressed features in this thesis. The dictionary formed with compressed
features has proved to be efficient for the classification task achieving comparable
results to the state of the art.
Next, this thesis addresses the more promising problem of simultaneous classifi-
cation of multiple actions. This is treated as matrix completion problem under
transduction setting. Matrix completion methods are considered as the generic
extension to the sparse representation methods from compressed sensing point
of view. The features and corresponding labels of the training and test data are
concatenated and placed as columns of a matrix. The unknown test labels would
be the missing entries in that matrix. This is solved using rank minimization
techniques based on the assumption that the underlying complete matrix would
be a low rank one. This approach has achieved results better than the state of the art on datasets with varying complexities.
This thesis then extends the matrix completion framework for joint classification
of actions to handle the missing features besides missing test labels. In
this context, deep features from a convolutional neural network are proposed.
A convolutional neural network is trained on the training data and features are
extracted from train and test data from the trained network. The performance
of the deep features has proved to be promising when compared to the state of
the art hand-crafted features
On the Power of Adaptivity in Matrix Completion and Approximation
We consider the related tasks of matrix completion and matrix approximation
from missing data and propose adaptive sampling procedures for both problems.
We show that adaptive sampling allows one to eliminate standard incoherence
assumptions on the matrix row space that are necessary for passive sampling
procedures. For exact recovery of a low-rank matrix, our algorithm judiciously
selects a few columns to observe in full and, with few additional measurements,
projects the remaining columns onto their span. This algorithm exactly recovers
an rank matrix using observations,
where is a coherence parameter on the column space of the matrix. In
addition to completely eliminating any row space assumptions that have pervaded
the literature, this algorithm enjoys a better sample complexity than any
existing matrix completion algorithm. To certify that this improvement is due
to adaptive sampling, we establish that row space coherence is necessary for
passive sampling algorithms to achieve non-trivial sample complexity bounds.
For constructing a low-rank approximation to a high-rank input matrix, we
propose a simple algorithm that thresholds the singular values of a zero-filled
version of the input matrix. The algorithm computes an approximation that is
nearly as good as the best rank- approximation using
samples, where is a slightly different coherence parameter on the matrix
columns. Again we eliminate assumptions on the row space
Low-Rank Matrix Approximation with Weights or Missing Data is NP-hard
Weighted low-rank approximation (WLRA), a dimensionality reduction technique
for data analysis, has been successfully used in several applications, such as
in collaborative filtering to design recommender systems or in computer vision
to recover structure from motion. In this paper, we study the computational
complexity of WLRA and prove that it is NP-hard to find an approximate
solution, even when a rank-one approximation is sought. Our proofs are based on
a reduction from the maximum-edge biclique problem, and apply to strictly
positive weights as well as binary weights (the latter corresponding to
low-rank matrix approximation with missing data).Comment: Proof of Lemma 4 (Lemma 3 in v1) has been corrected. Some remarks and
comments have been added. Accepted in SIAM Journal on Matrix Analysis and
Application
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