648 research outputs found
Blind Source Separation with Optimal Transport Non-negative Matrix Factorization
Optimal transport as a loss for machine learning optimization problems has
recently gained a lot of attention. Building upon recent advances in
computational optimal transport, we develop an optimal transport non-negative
matrix factorization (NMF) algorithm for supervised speech blind source
separation (BSS). Optimal transport allows us to design and leverage a cost
between short-time Fourier transform (STFT) spectrogram frequencies, which
takes into account how humans perceive sound. We give empirical evidence that
using our proposed optimal transport NMF leads to perceptually better results
than Euclidean NMF, for both isolated voice reconstruction and BSS tasks.
Finally, we demonstrate how to use optimal transport for cross domain sound
processing tasks, where frequencies represented in the input spectrograms may
be different from one spectrogram to another.Comment: 22 pages, 7 figures, 2 additional file
On Estimating Multi-Attribute Choice Preferences using Private Signals and Matrix Factorization
Revealed preference theory studies the possibility of modeling an agent's
revealed preferences and the construction of a consistent utility function.
However, modeling agent's choices over preference orderings is not always
practical and demands strong assumptions on human rationality and
data-acquisition abilities. Therefore, we propose a simple generative choice
model where agents are assumed to generate the choice probabilities based on
latent factor matrices that capture their choice evaluation across multiple
attributes. Since the multi-attribute evaluation is typically hidden within the
agent's psyche, we consider a signaling mechanism where agents are provided
with choice information through private signals, so that the agent's choices
provide more insight about his/her latent evaluation across multiple
attributes. We estimate the choice model via a novel multi-stage matrix
factorization algorithm that minimizes the average deviation of the factor
estimates from choice data. Simulation results are presented to validate the
estimation performance of our proposed algorithm.Comment: 6 pages, 2 figures, to be presented at CISS conferenc
Predicting passenger origin-destination in online taxi-hailing systems
Because of transportation planning, traffic management, and dispatch
optimization importance, passenger origin-destination prediction has become one
of the most important requirements for intelligent transportation systems
management. In this paper, we propose a model to predict the next specified
time window travels' origin and destination. To extract meaningful travel
flows, we use K-means clustering in four-dimensional space with maximum cluster
size limitation for origin and destination zones. Because of the large number
of clusters, we use non-negative matrix factorization to decrease the number of
travel clusters. Also, we use a stacked recurrent neural network model to
predict travel count in each cluster. Comparing our results with other existing
models shows that our proposed model has 5-7% lower mean absolute percentage
error (MAPE) for 1-hour time windows, and 14% lower MAPE for 30-minute time
windows.Comment: 25 pages, 20 figure
Retrospective Higher-Order Markov Processes for User Trails
Users form information trails as they browse the web, checkin with a
geolocation, rate items, or consume media. A common problem is to predict what
a user might do next for the purposes of guidance, recommendation, or
prefetching. First-order and higher-order Markov chains have been widely used
methods to study such sequences of data. First-order Markov chains are easy to
estimate, but lack accuracy when history matters. Higher-order Markov chains,
in contrast, have too many parameters and suffer from overfitting the training
data. Fitting these parameters with regularization and smoothing only offers
mild improvements. In this paper we propose the retrospective higher-order
Markov process (RHOMP) as a low-parameter model for such sequences. This model
is a special case of a higher-order Markov chain where the transitions depend
retrospectively on a single history state instead of an arbitrary combination
of history states. There are two immediate computational advantages: the number
of parameters is linear in the order of the Markov chain and the model can be
fit to large state spaces. Furthermore, by providing a specific structure to
the higher-order chain, RHOMPs improve the model accuracy by efficiently
utilizing history states without risks of overfitting the data. We demonstrate
how to estimate a RHOMP from data and we demonstrate the effectiveness of our
method on various real application datasets spanning geolocation data, review
sequences, and business locations. The RHOMP model uniformly outperforms
higher-order Markov chains, Kneser-Ney regularization, and tensor
factorizations in terms of prediction accuracy
A Generalisable Data Fusion Framework to Infer Mode of Transport Using Mobile Phone Data
Cities often lack up-to-date data analytics to evaluate and implement
transport planning interventions to achieve sustainability goals, as
traditional data sources are expensive, infrequent, and suffer from data
latency. Mobile phone data provide an inexpensive source of geospatial
information to capture human mobility at unprecedented geographic and temporal
granularity. This paper proposes a method to estimate updated mode of
transportation usage in a city, with novel usage of mobile phone application
traces to infer previously hard to detect modes, such as bikes and
ride-hailing/taxi. By using data fusion and matrix factorisation, we integrate
socioeconomic and demographic attributes of the local resident population into
the model. We tested the method in a case study of Santiago (Chile), and found
that changes from 2012 to 2020 in mode of transportation inferred by the method
are coherent with expectations from domain knowledge and the literature, such
as ride-hailing trips replacing mass transport.Comment: 19 pages, 8 figure
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