87,292 research outputs found
Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines
Hyperspectral imaging is a new remote sensing technique that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. Supervised classification of hyperspectral image data sets is a challenging problem due to the limited availability of training samples (which are very difficult and costly to obtain in practice) and the extremely high dimensionality of the data. In this paper, we explore the use of multi-channel morphological profiles for feature extraction prior to classification of remotely sensed hyperspectral data sets using support vector machines (SVMs). In order to introduce multi-channel morphological transformations, which rely on ordering of pixel vectors in multidimensional space, several vector ordering strategies are investigated. A reduced implementation which builds the multi-channel morphological profile based on the first components resulting from a dimensional reduction transformation applied to the input data is also proposed. Our experimental results, conducted using three representative hyperspectral data sets collected by NASA's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor and the German Digital Airborne Imaging Spectrometer (DAIS 7915), reveal that multi-channel morphological profiles can improve single-channel morphological profiles in the task of extracting relevant features for classification of hyperspectral data using small training sets
Augmented Tensor Decomposition with Stochastic Optimization
Tensor decompositions are powerful tools for dimensionality reduction and
feature interpretation of multidimensional data such as signals. Existing
tensor decomposition objectives (e.g., Frobenius norm) are designed for fitting
raw data under statistical assumptions, which may not align with downstream
classification tasks. Also, real-world tensor data are usually high-ordered and
have large dimensions with millions or billions of entries. Thus, it is
expensive to decompose the whole tensor with traditional algorithms. In
practice, raw tensor data also contains redundant information while data
augmentation techniques may be used to smooth out noise in samples. This paper
addresses the above challenges by proposing augmented tensor decomposition
(ATD), which effectively incorporates data augmentations to boost downstream
classification. To reduce the memory footprint of the decomposition, we propose
a stochastic algorithm that updates the factor matrices in a batch fashion. We
evaluate ATD on multiple signal datasets. It shows comparable or better
performance (e.g., up to 15% in accuracy) over self-supervised and autoencoder
baselines with less than 5% of model parameters, achieves 0.6% ~ 1.3% accuracy
gain over other tensor-based baselines, and reduces the memory footprint by 9X
when compared to standard tensor decomposition algorithms.Comment: Fixed some typo
Semantic coding by supervised dimensionality reduction
This paper addresses the problem of representing multimedia information under a compressed form that permits efficient classification. The semantic coding problem starts from a subspace method where dimensionality reduction is formulated as a matrix factorization problem. Data samples are jointly represented in a common subspace extracted from a redundant dictionary of basis functions. We first build on greedy pursuit algorithms for simultaneous sparse approximations to solve the dimensionality reduction problem. The method is extended into a supervised algorithm, which further encourages the class separability in the extraction of the most relevant features. The resulting supervised dimensionality reduction scheme provides an interesting trade-off between approximation (or compression) and discriminant feature extraction (or classification). The algorithm provides a compressed signal representation that can directly be used for multimedia data mining. The application of the proposed algorithm to image recognition problems further demonstrates classification performances that are competitive with state-of-the-art solutions in handwritten digit or face recognition. Semantic coding certainly represents an interesting solution to the challenging problem of processing huge volumes of multidimensional data in modern multimedia systems, where compressed data have to be processed and analyzed with limited computational complexity
Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare
For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover, complex applications may involve the temporal study of
several heterogeneous parameters. In that paper, we propose a method for mining
heterogeneous multivariate time-series for learning meaningful patterns. The
proposed approach allows for mixed time-series -- containing both pattern and
non-pattern data -- such as for imprecise matches, outliers, stretching and
global translating of patterns instances in time. We present the early results
of our approach in the context of monitoring the health status of a person at
home. The purpose is to build a behavioral profile of a person by analyzing the
time variations of several quantitative or qualitative parameters recorded
through a provision of sensors installed in the home
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
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