45,676 research outputs found
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models
Deep learning has shown state-of-art classification performance on datasets
such as ImageNet, which contain a single object in each image. However,
multi-object classification is far more challenging. We present a unified
framework which leverages the strengths of multiple machine learning methods,
viz deep learning, probabilistic models and kernel methods to obtain
state-of-art performance on Microsoft COCO, consisting of non-iconic images. We
incorporate contextual information in natural images through a conditional
latent tree probabilistic model (CLTM), where the object co-occurrences are
conditioned on the extracted fc7 features from pre-trained Imagenet CNN as
input. We learn the CLTM tree structure using conditional pairwise
probabilities for object co-occurrences, estimated through kernel methods, and
we learn its node and edge potentials by training a new 3-layer neural network,
which takes fc7 features as input. Object classification is carried out via
inference on the learnt conditional tree model, and we obtain significant gain
in precision-recall and F-measures on MS-COCO, especially for difficult object
categories. Moreover, the latent variables in the CLTM capture scene
information: the images with top activations for a latent node have common
themes such as being a grasslands or a food scene, and on on. In addition, we
show that a simple k-means clustering of the inferred latent nodes alone
significantly improves scene classification performance on the MIT-Indoor
dataset, without the need for any retraining, and without using scene labels
during training. Thus, we present a unified framework for multi-object
classification and unsupervised scene understanding
Distribution-Based Trajectory Clustering
Trajectory clustering enables the discovery of common patterns in trajectory
data. Current methods of trajectory clustering rely on a distance measure
between two points in order to measure the dissimilarity between two
trajectories. The distance measures employed have two challenges: high
computational cost and low fidelity. Independent of the distance measure
employed, existing clustering algorithms have another challenge: either
effectiveness issues or high time complexity. In this paper, we propose to use
a recent Isolation Distributional Kernel (IDK) as the main tool to meet all
three challenges. The new IDK-based clustering algorithm, called TIDKC, makes
full use of the distributional kernel for trajectory similarity measuring and
clustering. TIDKC identifies non-linearly separable clusters with irregular
shapes and varied densities in linear time. It does not rely on random
initialisation and is robust to outliers. An extensive evaluation on 7 large
real-world trajectory datasets confirms that IDK is more effective in capturing
complex structures in trajectories than traditional and deep learning-based
distance measures. Furthermore, the proposed TIDKC has superior clustering
performance and efficiency to existing trajectory clustering algorithms
Neural Collaborative Subspace Clustering
We introduce the Neural Collaborative Subspace Clustering, a neural model
that discovers clusters of data points drawn from a union of low-dimensional
subspaces. In contrast to previous attempts, our model runs without the aid of
spectral clustering. This makes our algorithm one of the kinds that can
gracefully scale to large datasets. At its heart, our neural model benefits
from a classifier which determines whether a pair of points lies on the same
subspace or not. Essential to our model is the construction of two affinity
matrices, one from the classifier and the other from a notion of subspace
self-expressiveness, to supervise training in a collaborative scheme. We
thoroughly assess and contrast the performance of our model against various
state-of-the-art clustering algorithms including deep subspace-based ones.Comment: Accepted to ICML 201
An overview of clustering methods with guidelines for application in mental health research
Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity
by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and
increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements.
In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and
implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic
models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently
introduced. How to choose algorithms to address common issues as well as methods for pre-clustering
data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general
guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms,
we provide information on R functions and librarie
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning
representations and simultaneously discovering cluster structure in unlabeled
data by optimizing a discriminative loss function. As opposed to supervised
deep learning, this line of research is in its infancy, and how to design and
optimize suitable loss functions to train deep neural networks for clustering
is still an open question. Our contribution to this emerging field is a new
deep clustering network that leverages the discriminative power of
information-theoretic divergence measures, which have been shown to be
effective in traditional clustering. We propose a novel loss function that
incorporates geometric regularization constraints, thus avoiding degenerate
structures of the resulting clustering partition. Experiments on synthetic
benchmarks and real datasets show that the proposed network achieves
competitive performance with respect to other state-of-the-art methods, scales
well to large datasets, and does not require pre-training steps
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