Block Coordinate Descent for Sparse NMF

Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural measure of sparsity is the L$_0$ norm, however its optimization is NP-hard. Mixed norms, such as L$_1$/L$_2$ measure, have been shown to model sparsity robustly, based on intuitive attributes that such measures need to satisfy. This is in contrast to computationally cheaper alternatives such as the plain L$_1$ norm. However, present algorithms designed for optimizing the mixed norm L$_1$/L$_2$ are slow and other formulations for sparse NMF have been proposed such as those based on L$_1$ and L$_0$ norms. Our proposed algorithm allows us to solve the mixed norm sparsity constraints while not sacrificing computation time. We present experimental evidence on real-world datasets that shows our new algorithm performs an order of magnitude faster compared to the current state-of-the-art solvers optimizing the mixed norm and is suitable for large-scale datasets

Low dark current InAs/GaSb type-II superlattice infrared photodetectors with resonant tunnelling filters

InAs/GaSb type-II strained-layer superlattice (SLS) photovoltaic infrared (IR) detectors are currently of great interest for mid- and long-wave IR detection. A novel technique of reducing detector dark current by inserting resonant tunnelling barriers into a conventional InAs/GaSb SLS is investigated. The GaSb/InAs/GaSb resonant tunnelling double barrier heterostructure was designed to be periodically inserted into a conventional InAs/GaSb SLS detector to block thermally excited electrons, while permitting photo-excited electrons to tunnel through. The measured dark current density of the tunnelling InAs/GaSb SLS detector in the entire negative bias range is lower than that of the conventional SLS detector by a factor of about 3.8 at 77 K. At 84 K, the Johnson-noise-limited detectivity of the tunnelling detector, measured at 4 µm, is 18% higher than that of the conventional detector. Both the conventional and the tunnelling SLS detectors demonstrated high-temperature operation, up to 300 K.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58092/2/d6_23_015.pd

Sequential Sparse NMF

Nonnegative Matrix Factorization (NMF) is a standard tool for data analysis. An important variant is the Sparse NMF problem. A natural measure of sparsity is the L₀ norm, however its optimization is NP-hard. Here, we consider a sparsity measure linear in the ratio of the L₁ and L₂ norms, and propose an efficient algorithm to handle the norm constraints which arise when optimizing this measure. Although algorithms for solving these are available, they are typically inefficient. We present experimental evidence that our new algorithm performs an order of magnitude faster compared to the previous state-of-the-art

Sequential Sparse NMF

Nonnegative Matrix Factorization (NMF) is a standard tool for data analysis. An important variant is the Sparse NMF problem. A natural measure of sparsity is the L₀ norm, however its optimization is NP-hard. Here, we consider a sparsity measure linear in the ratio of the L₁ and L₂ norms, and propose an efficient algorithm to handle the norm constraints which arise when optimizing this measure. Although algorithms for solving these are available, they are typically inefficient. We present experimental evidence that our new algorithm performs an order of magnitude faster compared to the previous state-of-the-art

Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence

A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall short of proving this claim and that the ICA algorithms are indeed doing what they are designed to do: identify maximally independent sources

Quantum Size Effects on the Chemical Sensing Performance of Two-Dimensional Semiconductors

We investigate the role of quantum confinement on the performance of gas sensors based on two-dimensional InAs membranes. Pd-decorated InAs membranes configured as H2 sensors are shown to exhibit strong thickness dependence, with ~100x enhancement in the sensor response as the thickness is reduced from 48 to 8 nm. Through detailed experiments and modeling, the thickness scaling trend is attributed to the quantization of electrons which favorably alters both the position and the transport properties of charge carriers; thus making them more susceptible to surface phenomena