40,062 research outputs found
Kernel Manifold Alignment
We introduce a kernel method for manifold alignment (KEMA) and domain
adaptation that can match an arbitrary number of data sources without needing
corresponding pairs, just few labeled examples in all domains. KEMA has
interesting properties: 1) it generalizes other manifold alignment methods, 2)
it can align manifolds of very different complexities, performing a sort of
manifold unfolding plus alignment, 3) it can define a domain-specific metric to
cope with multimodal specificities, 4) it can align data spaces of different
dimensionality, 5) it is robust to strong nonlinear feature deformations, and
6) it is closed-form invertible which allows transfer across-domains and data
synthesis. We also present a reduced-rank version for computational efficiency
and discuss the generalization performance of KEMA under Rademacher principles
of stability. KEMA exhibits very good performance over competing methods in
synthetic examples, visual object recognition and recognition of facial
expressions tasks
Difference image photometry with bright variable backgrounds
Over the last two decades the Andromeda Galaxy (M31) has been something of a
test-bed for methods aimed at obtaining accurate time-domain relative
photometry within highly crowded fields. Difference imaging methods, originally
pioneered towards M31, have evolved into sophisticated methods, such as the
Optimal Image Subtraction (OIS) method of Alard & Lupton (1998), that today are
most widely used to survey variable stars, transients and microlensing events
in our own Galaxy. We show that modern difference image (DIA) algorithms such
as OIS, whilst spectacularly successful towards the Milky Way bulge, may
perform badly towards high surface brightness targets such as the M31 bulge.
Poor results can occur in the presence of common systematics which add spurious
flux contributions to images, such as internal reflections, scattered light or
fringing. Using data from the Angstrom Project microlensing survey of the M31
bulge, we show that very good results are usually obtainable by first
performing careful photometric alignment prior to using OIS to perform
point-spread function (PSF) matching. This separation of background matching
and PSF matching, a common feature of earlier M31 photometry techniques, allows
us to take full advantage of the powerful PSF matching flexibility offered by
OIS towards high surface brightness targets. We find that difference images
produced this way have noise distributions close to Gaussian, showing
significant improvement upon results achieved using OIS alone. We show that
with this correction light-curves of variable stars and transients can be
recovered to within ~10 arcseconds of the M31 nucleus. Our method is simple to
implement and is quick enough to be incorporated within real-time DIA
pipelines. (Abridged)Comment: 12 pages. Accepted for publication in MNRAS. Includes an expanded
discussion of DIA testing and results, including additional lightcurve
example
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