Innovative Optimization Techniques in Machine Learning Algorithms

Today, you live in the era of automation, artificial intelligence, and the rise of complicated digitization. 

The rapid advancement of smart technologies appears to have been going faster as the years go by.

Businesses across the US and around the globe have benefitted dearly from this accelerated technological progress by implementing it throughout their operations as suitable. 

Moreover, using this technology to speed up operations is a valid point to remember; it is a completely different ball game to use modern tech for better performance.

If your approaches are clear, you can expect better usability from your existing resources. 

This is true for machine learning as for everything else. So, today let’s highlight some of the top ways to optimize ML algorithms in detail.

The Need For Machine Learning Optimization & Its Types

In the world of machine learning, optimization is an activity of adjusting the model parameters to maximize or minimize some objective function. 

The function of an objective function is to reduce the error on a specific set of training data. 

Usually, when you develop a program, you determine how it should consider things, which remains like that.

Business decisions can be made by machine learning for new data according to the given program by adjusting the rules. 

That’s pretty much it if you’d simplify the concept. But of course, there’s a lot that goes on within the program that gets complex and complicated. 

The algorithms you use for these programs assist you in identifying the best settings for a model’s parameters that reduce a specific function, usually referred to as a loss function.

This loss function shows errors in the model or how methodically it completes an assigned task.

Given that, your approach can be better or worse based on your goals and circumstances.

At last, to generate productive intelligent programs, you need to have effective machine learning, so it’s recommended to boost your algorithms for good results.  

So, let’s closely look at some possible ways to improve machine learning algorithms:

Gradient Descent Optimization

Let’s first discuss gradient descent. This is a primary ML optimization method. As the name implies, it has to do with gradients and descents. As a result of its simplicity, it’s one of the reputed techniques.

What it does is that it minimizes the loss function through gradual steps toward the steepest descent. Of course, it does calculate the gradient pointing to that first.

Later, with every replication, you observe it moving closer and closer to the descent. Eventually, your algorithm’s performance will become better. But where there’s a way, nobody said there’d only be one way.

So, as you can guess, there are multiple gradient descent optimization methods, such as:

Stochastic Gradient Descent (SGD)

This is a relatively quicker variant, suitable for the fast-paced processing of extensive data.

Mini-batch Gradient Descent

It requires less data compared to SGD and leads to more organized results. 

Adam, AdaGrad, RMSprop

Finally, you have these three, which improve upon SDG’s convergence and gradient vanishing issues.

Newton and Quasi-Newton Optimizations

It uses something you define as second-order information, which minimizes your algorithm’s complexity better than gradient descent. That does come at a price, quite rigorously.

An arguable improvement upon this method is known as the Quasi-Newton Method.

Here, you can use the Hessian matrix as the second-order derivative that improves upon the Newton method’s loss function calculations. 

This can be done in two ways:

BFGS (Broyden-Fletcher-Goldfarb-Shanno) 

The BFGS variant relies on rank-one updates for the Hessian matrix calculations.

Limited-memory BFGS (L-BFGS)

The L-BFGS is a more memory-efficient edition of the BFGS Quasi-Newton method.

Evolutionary Algorithms

When things aren’t working out well as they should, you need to adapt to make it right. Or else, you should accept the new challenges and handle them better. 

Here, you have to invest some time in tackling some new challenges properly. A similar concept applies to advanced machine learning algorithms.

When you can’t trust the gradient information you have, evolutionary algorithms should be your go-to optimization choice. 

These work well by imitating an array of solutions and expanding for improved performance via several assessment factors. The best examples of this are Differential Evolution and Genetic Algorithms.

Swarm Intelligence Algorithms

When the cure was finally implemented on a global scale for the last pandemic, many people hadn’t gotten their shots.

Yet, they weren’t getting sick, and healthcare professionals termed the scenario “herd immunity,” implying the effects of some people getting cured passed on to others in the group.

That’s not entirely related to the concept of swarm intelligence algorithms. But these optimization methods do also go by the concept of grouping things for collective goals.

For instance, the Particle Swarm Optimization (PSO) method was created to iterate a swarm of solutions to find the best approach to a machine learning problem you’re tackling. 

Bayesian Optimization

All machine learning algorithms are good for what they’re designed for. But what happens when you need hyperparameter tuning? This is where you turn to Bayesian optimization.

You’re often short on time for landing the best solutions by a guessing game.

With this algorithm, you can go with the most ideal points for forecasting the superior next solution according to the probabilistic modeling of your object function.

As a result, you fine-tune your approach to black-box function optimizations, which would otherwise have taken you considerably longer than you’d anticipated.

Coordinate Descent

Coordinate Descent is one of the familiar machine learning optimization techniques. It differs from gradient descent in the primary approach to solving the problem. 

This algorithm improves one parameter in one shot and maintains the others fixed. This makes it additionally advantageous for problems where the loss function is completely separable for different parameters.
So, you can easily use this model for sparse linear models like LASSO (Least Absolute Shrinkage and Selection Operator) without a second thought.

Emerging Trends in Optimization Techniques

What’s here is what works. But what’s upcoming is the future.

This is why when you realize “machine learning optimization,” you can’t depend solely on what has brought results but also on how machine learning and artificial intelligence continue to evolve. 

Let’s explore common trends that have a crucial impact on the future of optimization in machine learning:

AutoML

It’s already mentioned as one of the few methods involved in automated learning (bayesian optimization).

A primary application of AutoML (Automated Machine Learning) is to accelerate the streamlining of real-world problem-solving.

For example, it’s widely used to implement ML automation across different operations containing model selection and feature engineering. 

But much more that goes on here than can be covered in a single paragraph.

Optimization for Quantum Computing

Filmmakers and especially science-fiction filmmakers love using the word “quantum” when they don’t feel like explaining the concepts they’re using in their movies. 

Either way, there’s a thing called Quantum Approximate Optimization Algorithm, or QAOA in short, which is one of the many machine learning algorithms developed to optimize complex quantum computations for enhanced speeds and more accurate results. 

Robust & Adversarial Optimization

The name doesn’t suggest other optimization algorithms are bad, only that this one helps tackle noise and adversarial issues much better.

You can’t predict the type of data you’ll need to run your algorithm against.

At times, it can be too flawed, so robust optimization models come into play here. They are mainly developed to ensure that your ML algorithm remains adaptable under the least favourable circumstances. 

Finding the Right Balance

If you aim to find one algorithm that does it and is the best at everything for everything, I’ve got some bad news for you.

Every machine learning optimization algorithm described in this post has its ups and downs. There’s no black and white. Of course, some might perform better for you than others, based on your needs.

So, your responsibility is to analyze your options and, based on that, decide what suits your optimization needs.

For this, if you need any consultation, contact Express Analytics; otherwise, you can consider the below-mentioned factors:

1. The kind of ML model (e.g., neural network or linear regression)
2. The size and complexity of the dataset
3. The expected level of accuracy and convergence speed
4. Computational resources available

Ultimately, the final decision will be yours only. So, consider your options, try a couple of variations here and there, and make an informed decision.

Keep in mind that the right balance is important to improve your outcomes perfectly. Happy optimizing, and follow for more.