54 research outputs found
On the Persistence of Clustering Solutions and True Number of Clusters in a Dataset
Typically clustering algorithms provide clustering solutions with
prespecified number of clusters. The lack of a priori knowledge on the true
number of underlying clusters in the dataset makes it important to have a
metric to compare the clustering solutions with different number of clusters.
This article quantifies a notion of persistence of clustering solutions that
enables comparing solutions with different number of clusters. The persistence
relates to the range of data-resolution scales over which a clustering solution
persists; it is quantified in terms of the maximum over two-norms of all the
associated cluster-covariance matrices. Thus we associate a persistence value
for each element in a set of clustering solutions with different number of
clusters. We show that the datasets where natural clusters are a priori known,
the clustering solutions that identify the natural clusters are most persistent
- in this way, this notion can be used to identify solutions with true number
of clusters. Detailed experiments on a variety of standard and synthetic
datasets demonstrate that the proposed persistence-based indicator outperforms
the existing approaches, such as, gap-statistic method, -means, -means,
-means, dip-means algorithms and information-theoretic method, in
accurately identifying the clustering solutions with true number of clusters.
Interestingly, our method can be explained in terms of the phase-transition
phenomenon in the deterministic annealing algorithm, where the number of
distinct cluster centers changes (bifurcates) with respect to an annealing
parameter
Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework
We present a framework to address a class of sequential decision making
problems. Our framework features learning the optimal control policy with
robustness to noisy data, determining the unknown state and action parameters,
and performing sensitivity analysis with respect to problem parameters. We
consider two broad categories of sequential decision making problems modelled
as infinite horizon Markov Decision Processes (MDPs) with (and without) an
absorbing state. The central idea underlying our framework is to quantify
exploration in terms of the Shannon Entropy of the trajectories under the MDP
and determine the stochastic policy that maximizes it while guaranteeing a low
value of the expected cost along a trajectory. This resulting policy enhances
the quality of exploration early on in the learning process, and consequently
allows faster convergence rates and robust solutions even in the presence of
noisy data as demonstrated in our comparisons to popular algorithms such as
Q-learning, Double Q-learning and entropy regularized Soft Q-learning. The
framework extends to the class of parameterized MDP and RL problems, where
states and actions are parameter dependent, and the objective is to determine
the optimal parameters along with the corresponding optimal policy. Here, the
associated cost function can possibly be non-convex with multiple poor local
minima. Simulation results applied to a 5G small cell network problem
demonstrate successful determination of communication routes and the small cell
locations. We also obtain sensitivity measures to problem parameters and
robustness to noisy environment data.Comment: 17 pages, 7 figure
Sparse Linear Regression with Constraints: A Flexible Entropy-based Framework
This work presents a new approach to solve the sparse linear regression
problem, i.e., to determine a k-sparse vector w in R^d that minimizes the cost
||y - Aw||^2_2. In contrast to the existing methods, our proposed approach
splits this k-sparse vector into two parts -- (a) a column stochastic binary
matrix V, and (b) a vector x in R^k. Here, the binary matrix V encodes the
location of the k non-zero entries in w. Equivalently, it encodes the subset of
k columns in the matrix A that map w to y. We demonstrate that this enables
modeling several non-trivial application-specific structural constraints on w
as constraints on V. The vector x comprises of the actual non-zero values in w.
We use Maximum Entropy Principle (MEP) to solve the resulting optimization
problem. In particular, we ascribe a probability distribution to the set of all
feasible binary matrices V, and iteratively determine this distribution and the
vector x such that the associated Shannon entropy gets minimized, and the
regression cost attains a pre-specified value. The resulting algorithm employs
homotopy from the convex entropy function to the non-convex cost function to
avoid poor local minimum. We demonstrate the efficacy and flexibility of our
proposed approach in incorporating a variety of practical constraints, that are
otherwise difficult to model using the existing benchmark methods
A Dual System-Level Parameterization for Identification from Closed-Loop Data
This work presents a dual system-level parameterization (D-SLP) method for
closed-loop system identification. The recent system-level synthesis framework
parameterizes all stabilizing controllers via linear constraints on closed-loop
response functions, known as system-level parameters. It was demonstrated that
several structural, locality, and communication constraints on the controller
can be posed as convex constraints on these system-level parameters. In the
current work, the identification problem is treated as a {\em dual} of the
system-level synthesis problem. The plant model is identified from the dual
system-level parameters associated to the plant. In comparison to existing
closed-loop identification approaches (such as the dual-Youla
parameterization), the D-SLP framework neither requires the knowledge of a
nominal plant that is stabilized by the known controller, nor depends upon the
choice of factorization of the nominal plant and the stabilizing controller.
Numerical simulations demonstrate the efficacy of the proposed D-SLP method in
terms of identification errors, compared to existing closed-loop identification
techniques
Closed-Loop Identification of Stabilized Models Using Dual Input-Output Parameterization
This paper introduces a dual input-output parameterization (dual IOP) for the
identification of linear time-invariant systems from closed-loop data. It draws
inspiration from the recent input-output parameterization developed to
synthesize a stabilizing controller. The controller is parameterized in terms
of closed-loop transfer functions, from the external disturbances to the input
and output of the system, constrained to lie in a given subspace. Analogously,
the dual IOP method parameterizes the unknown plant with analogous closed-loop
transfer functions, also referred to as dual parameters. In this case, these
closed-loop transfer functions are constrained to lie in an affine subspace
guaranteeing that the identified plant is \emph{stabilized} by the known
controller. Compared with existing closed-loop identification techniques
guaranteeing closed-loop stability, such as the dual Youla parameterization,
the dual IOP neither requires a doubly-coprime factorization of the controller
nor a nominal plant that is stabilized by the controller. The dual IOP does not
depend on the order and the state-space realization of the controller either,
as in the dual system-level parameterization. Simulation shows that the dual
IOP outperforms the existing benchmark methods
Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint
The industrial drying process consumes approximately 12% of the total energy
used in manufacturing, with the potential for a 40% reduction in energy usage
through improved process controls and the development of new drying
technologies. To achieve cost-efficient and high-performing drying, multiple
drying technologies can be combined in a modular fashion with optimal
sequencing and control parameters for each. This paper presents a mathematical
formulation of this optimization problem and proposes a framework based on the
Maximum Entropy Principle (MEP) to simultaneously solve for both optimal values
of control parameters and optimal sequence. The proposed algorithm addresses
the combinatorial optimization problem with a non-convex cost function riddled
with multiple poor local minima. Simulation results on drying distillers dried
grain (DDG) products show up to 12% improvement in energy consumption compared
to the most efficient single-stage drying process. The proposed algorithm
converges to local minima and is designed heuristically to reach the global
minimum
Ethics-Relevant Values as Antecedents of Personality Change: Longitudinal Findings from the Life and Time Study
What leads personality to develop in adulthood? Values, guiding principles that apply across contexts, may capture motivation for growth and change. An essentialist trait perspective posits that personality changes only as a result of organic factors. But evidence suggests that psychosocial factors also influence personality change, especially during young adulthood. In the Life and Time study of sources of personality change in adulthood, we specifically explore ethically-relevant value priorities, those related to the relative prioritization of narrow self-interest over the concerns of a larger community. According to Rollo May (1967), “mature values”, including aspects of both self-transcendence and self-determination, should serve to diminish or prevent neurotic anxiety. This is consistent with research on materialism, which is associated with lower well-being. An index based on May’s proposal and several related constructs (materialism, unmitigated self-interest, collectivism and individualism) are tested longitudinally as possible antecedents of Big Five/Six personality trait change using bivariate LCMSR models in a national community sample (N = 864 at Time 1). Contrary to an essentialist trait perspective, these value priorities more often preceded change in personality traits than vice-versa. Somewhat consistent with May’s theory, higher “mature” values preceded higher openness (statistically significant at the p < .005 level). Higher vertical individualism significantly preceded lower compassion, intellect and openness. At the suggestive (p < .05) level, higher unmitigated self-interest preceded lower conscientiousness, higher vertical individualism preceded higher volatility, higher mature values preceded higher honesty/propriety and politeness, higher horizontal collectivism preceded higher orderliness, agreeableness, and assertiveness and lower intellect, and higher horizontal individualism preceded lower withdrawal. In two of three cases, suggestive personality-as-antecedent-of-values-change effects were reciprocal with the values-effects: higher conscientiousness scores reciprocally preceded lower unmitigated self-interest, and higher volatility higher vertical individualism. No significant or suggestive “stand-alone”, non-reciprocal personality on values effects were found
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