74,379 research outputs found
You Are What You Eat: A Preference-Aware Inverse Optimization Approach
A key challenge in the emerging field of precision nutrition entails
providing diet recommendations that reflect both the (often unknown) dietary
preferences of different patient groups and known dietary constraints specified
by human experts. Motivated by this challenge, we develop a preference-aware
constrained-inference approach in which the objective function of an
optimization problem is not pre-specified and can differ across various
segments. Among existing methods, clustering models from machine learning are
not naturally suited for recovering the constrained optimization problems,
whereas constrained inference models such as inverse optimization do not
explicitly address non-homogeneity in given datasets. By harnessing the
strengths of both clustering and inverse optimization techniques, we develop a
novel approach that recovers the utility functions of a constrained
optimization process across clusters while providing optimal diet
recommendations as cluster representatives. Using a dataset of patients' daily
food intakes, we show how our approach generalizes stand-alone clustering and
inverse optimization approaches in terms of adherence to dietary guidelines and
partitioning observations, respectively. The approach makes diet
recommendations by incorporating both patient preferences and expert
recommendations for healthier diets, leading to structural improvements in both
patient partitioning and nutritional recommendations for each cluster. An
appealing feature of our method is its ability to consider infeasible but
informative observations for a given set of dietary constraints. The resulting
recommendations correspond to a broader range of dietary options, even when
they limit unhealthy choices
Deep Constrained Dominant Sets for Person Re-Identification
In this work, we propose an end-to-end constrained clustering scheme to tackle the person re-identification (re-id) problem. Deep neural networks (DNN) have recently proven to be effective on person re-identification task. In particular, rather than leveraging solely a probe-gallery similarity, diffusing the similarities among the gallery images in an end-to-end manner has proven to be effective in yielding a robust probe-gallery affinity. However, existing methods do not apply probe image as a constraint, and are prone to noise propagation during the similarity diffusion process. To overcome this, we propose an intriguing scheme which treats person-image retrieval problem as a constrained clustering optimization problem, called deep constrained dominant sets (DCDS). Given a probe and gallery images, we re-formulate person re-id problem as finding a constrained cluster, where the probe image is taken as a constraint (seed) and each cluster corresponds to a set of images corresponding to the same person. By optimizing the constrained clustering in an end-to-end manner, we naturally leverage the contextual knowledge of a set of images corresponding to the given person-images. We further enhance the performance by integrating an auxiliary net alongside DCDS, which employs a multi-scale ResNet. To validate the effectiveness of our method we present experiments on several benchmark datasets and show that the proposed method can outperform state-of-the-art methods
Clustering Multiple Sclerosis Medication Sequence Data with Mixture Markov Chain Analysis with covariates using Multiple Simplex Constrained Optimization Routine (MSiCOR)
Multiple sclerosis (MS) is an autoimmune disease of the central nervous
system that causes neurodegeneration. While disease-modifying therapies (DMTs)
reduce inflammatory disease activity and delay worsening disability in MS,
there are significantly varying treatment responses across people with MS
(pwMS). pwMS often receive serial monotherapies of DMTs. Here, we propose a
novel method to cluster pwMS according to the sequence of DMT prescriptions and
associated clinical features (covariates). This is achieved via a mixture
Markov chain analysis with covariates, where the sequence of prescribed DMTs
for each patient is modeled as a Markov chain. Given the computational
challenges to maximize the mixture likelihood on the constrained parameter
space, we develop a pattern search-based global optimization technique which
can optimize any objective function on a collection of simplexes and shown to
outperform other related global optimization techniques. In simulation
experiments, the proposed method is shown to outperform the
Expectation-Maximization (EM) algorithm based method for clustering sequence
data without covariates. Based on the analysis, we divided MS patients into 3
clusters: inferon-beta dominated, multi-DMTs, and natalizumab dominated.
Further cluster-specific summaries of relevant covariates indicate patient
differences among the clusters. This method may guide the DMT prescription
sequence based on clinical features
Constrained clustering: general pairwise and cardinality constraints
In this work, we study constrained clustering, where constraints are utilized to guide the clustering process. In existing works, two categories of constraints have been widely explored, namely pairwise and cardinality constraints. Pairwise constraints enforce the cluster labels of two instances to be the same (must-link constraints) or different (cannot-link constraints). Cardinality constraints encourage cluster sizes to satisfy a user-specified distribution. However, most existing constrained clustering models can only utilize one category of constraints at a time. In this paper, we enforce the above two categories into a unified clustering model starting with the integer program formulation of the standard K-means. As these two categories provide useful information at different levels, utilizing both of them is expected to allow for better clustering performance. However, the optimization is difficult due to the binary and quadratic constraints in the proposed unified formulation. To alleviate this difficulty, we utilize two techniques: equivalently replacing the binary constraints by the intersection of two continuous constraints; the other is transforming the quadratic constraints into bi-linear constraints by introducing extra variables. Then we derive an equivalent continuous reformulation with simple constraints, which can be efficiently solved by Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experiments on both synthetic and real data demonstrate: 1) when utilizing a single category of constraint, the proposed model is superior to or competitive with state-of-the-art constrained clustering models, and 2) when utilizing both categories of constraints jointly, the proposed model shows better performance than the case of the single category. The experimental results show that the proposed method exploits the constraints to achieve perfect clustering performance with improved clustering to 2-5 % in classical clustering metrics, e.g., Adjusted Random Index (ARI), Mirkin's Index (MI), and Huber's Index (HI), outerperfomring all compared-againts methods across the board. Moreover, we show that our method is robust to initialization
Constrained K-means with General Pairwise and Cardinality Constraints
In this work, we study constrained clustering, where constraints are utilized
to guide the clustering process. In existing works, two categories of
constraints have been widely explored, namely pairwise and cardinality
constraints. Pairwise constraints enforce the cluster labels of two instances
to be the same (must-link constraints) or different (cannot-link constraints).
Cardinality constraints encourage cluster sizes to satisfy a user-specified
distribution. However, most existing constrained clustering models can only
utilize one category of constraints at a time. In this paper, we enforce the
above two categories into a unified clustering model starting with the integer
program formulation of the standard K-means. As these two categories provide
useful information at different levels, utilizing both of them is expected to
allow for better clustering performance. However, the optimization is difficult
due to the binary and quadratic constraints in the proposed unified
formulation. To alleviate this difficulty, we utilize two techniques:
equivalently replacing the binary constraints by the intersection of two
continuous constraints; the other is transforming the quadratic constraints
into bi-linear constraints by introducing extra variables. Then we derive an
equivalent continuous reformulation with simple constraints, which can be
efficiently solved by Alternating Direction Method of Multipliers (ADMM)
algorithm. Extensive experiments on both synthetic and real data demonstrate:
(1) when utilizing a single category of constraint, the proposed model is
superior to or competitive with state-of-the-art constrained clustering models,
and (2) when utilizing both categories of constraints jointly, the proposed
model shows better performance than the case of the single category
Low rank methods for optimizing clustering
Complex optimization models and problems in machine learning often have the majority of information in a low rank subspace. By careful exploitation of these low rank structures in clustering problems, we find new optimization approaches that reduce the memory and computational cost.
We discuss two cases where this arises. First, we consider the NEO-K-Means (Non-Exhaustive, Overlapping K-Means) objective as a way to address overlapping and outliers in an integrated fashion. Optimizing this discrete objective is NP-hard, and even though there is a convex relaxation of the objective, straightforward convex optimization approaches are too expensive for large datasets. We utilize low rank structures in the solution matrix of the convex formulation and use a low-rank factorization of the solution matrix directly as a practical alternative. The resulting optimization problem is non-convex, but has a smaller number of solution variables, and can be locally optimized using an augmented Lagrangian method. In addition, we consider two fast multiplier methods to accelerate the convergence of the augmented Lagrangian scheme: a proximal method of multipliers and an alternating direction method of multipliers. For the proximal augmented Lagrangian, we show a convergence result for the non-convex case with bound-constrained subproblems. When the clustering performance is evaluated on real-world datasets, we show this technique is effective in finding the ground-truth clusters and cohesive overlapping communities in real-world networks.
The second case is where the low-rank structure appears in the objective function. Inspired by low rank matrix completion techniques, we propose a low rank symmetric matrix completion scheme to approximate a kernel matrix. For the kernel k-means problem, we show empirically that the clustering performance with the approximation is comparable to the full kernel k-means
- …