2,557 research outputs found
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
Kernel discriminant analysis and clustering with parsimonious Gaussian process models
This work presents a family of parsimonious Gaussian process models which
allow to build, from a finite sample, a model-based classifier in an infinite
dimensional space. The proposed parsimonious models are obtained by
constraining the eigen-decomposition of the Gaussian processes modeling each
class. This allows in particular to use non-linear mapping functions which
project the observations into infinite dimensional spaces. It is also
demonstrated that the building of the classifier can be directly done from the
observation space through a kernel function. The proposed classification method
is thus able to classify data of various types such as categorical data,
functional data or networks. Furthermore, it is possible to classify mixed data
by combining different kernels. The methodology is as well extended to the
unsupervised classification case. Experimental results on various data sets
demonstrate the effectiveness of the proposed method
Data Visualization, Dimensionality Reduction, and Data Alignment via Manifold Learning
The high dimensionality of modern data introduces significant challenges in descriptive and exploratory data analysis. These challenges gave rise to extensive work on dimensionality reduction and manifold learning aiming to provide low dimensional representations that preserve or uncover intrinsic patterns and structures in the data. In this thesis, we expand the current literature in manifold learning developing two methods called DIG (Dynamical Information Geometry) and GRAE (Geometry Regularized Autoencoders). DIG is a method capable of finding low-dimensional representations of high-frequency multivariate time series data, especially suited for visualization. GRAE is a general framework which splices the well-established machinery from kernel manifold learning methods to recover a sensitive geometry, alongside the parametric structure of autoencoders.
Manifold learning can also be useful to study data collected from different measurement instruments, conditions, or protocols of the same underlying system. In such cases the data is acquired in a multi-domain representation. The last two Chapters of this thesis are devoted to two new methods capable of aligning multi-domain data, leveraging their geometric structure alongside limited common information. First, we present DTA (Diffusion Transport Alignment), a semi-supervised manifold alignment method that exploits prior one-to-one correspondence knowledge between distinct data views and finds an aligned common representation. And finally, we introduce MALI (Manifold Alignment with Label Information). Here we drop the one-to-one prior correspondences assumption, since in many scenarios such information can not be provided, either due to the nature of the experimental design, or it becomes extremely costly. Instead, MALI only needs side-information in the form of discrete labels/classes present in both domains
Self-supervised learning in non-small cell lung cancer discovers novel morphological clusters linked to patient outcome and molecular phenotypes
Histopathological images provide the definitive source of cancer diagnosis,
containing information used by pathologists to identify and subclassify
malignant disease, and to guide therapeutic choices. These images contain vast
amounts of information, much of which is currently unavailable to human
interpretation. Supervised deep learning approaches have been powerful for
classification tasks, but they are inherently limited by the cost and quality
of annotations. Therefore, we developed Histomorphological Phenotype Learning,
an unsupervised methodology, which requires no annotations and operates via the
self-discovery of discriminatory image features in small image tiles. Tiles are
grouped into morphologically similar clusters which appear to represent
recurrent modes of tumor growth emerging under natural selection. These
clusters have distinct features which can be identified using orthogonal
methods. Applied to lung cancer tissues, we show that they align closely with
patient outcomes, with histopathologically recognised tumor types and growth
patterns, and with transcriptomic measures of immunophenotype
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